We’ll unravel how this technique helps ensure every line of code is scrutinized and put to the test. Whether you’re a seasoned developer or a curious tech enthusiast, this blog promises valuable insights into enhancing code quality and reliability.

Get ready to sharpen your testing arsenal and elevate your software craftsmanship!

What is Statement Coverage Testing?

A fundamental method of software testing called “statement coverage testing” makes sure that every statement in a piece of code is run at least once in order to gauge how thorough the testing was. This method offers useful insights into how thoroughly a program’s source code has been checked by monitoring the execution of each line of code.

How to Measure Statement Coverage?

When comparing the number of executed statements to the total number of statements in the code, statement coverage is calculated. Statement coverage is calculated as follows:

Since this evaluation is given as a percentage, testers can determine what fraction of the code has really been used during testing.

Suppose we have a code snippet with 10 statements, and during testing, 7 of these statements are executed.

def calculate_average(numbers):
total = 0
count = 0
for num in numbers:
total += num
count += 1
if count > 0:
average = total / count
else:
average = 0
return average

In this case:

Number of Executed Statements: 7
Total Number of Statements: 10
Using the formula for statement coverage:

Statement Coverage = (Number of Executed Statements / Total Number of Statements) * 100%
Statement Coverage = (7 / 10) * 100% = 70%

Therefore, this code snippet’s statement coverage is 70%. This shows that during testing, 70% of the code’s statements were carried out.

To ensure a more thorough testing of the software, it’s critical to aim for higher statement coverage. In order to thoroughly evaluate the quality of the code, additional coverage metrics like branch coverage and path coverage are also essential.

Achieving 100% statement coverage, however, does not guarantee that all scenarios have been tested.

Example of Statement Coverage Testing:

Let’s consider a simple code snippet to illustrate statement coverage:

def calculate_sum(a, b):

    if a > b:

        result = a + b

    else:

        result = a – b

    return result

Suppose we have a test suite with two test cases:

1. calculate_sum(5, 3)
2. calculate_sum(3, 5)

Both the ‘if ‘ and ‘else’ branches are executed when these test cases are applied to the function, covering all the code statements. As a result, all statements in the code have been tested, as shown by the 100% statement coverage.

Statement coverage testing makes ensuring that no lines of code are left untested and adds to the software’s overall stability.

It’s crucial to remember, though, that while it offers a basic level of coverage assessment, having high statement coverage doesn’t imply that there won’t be any errors or rigorous testing.

For a more thorough evaluation of code quality, other methods like branch coverage and path coverage may be required.

Advantages and disadvantages of statement coverage testing

Statement Coverage Testing Benefits/Advantages

Detailed Code Inspection:

Statement Coverage Testing makes sure that each line of code is run at least once during testing.

This facilitates the discovery of any untested code segments and guarantees a more thorough evaluation of the product.

Consider a financial application where testing statement coverage reveals that a certain calculation module has not been tested, requiring further testing to cover it.

Quick Dead Code Detection:

By immediately identifying dead or inaccessible code, statement coverage enables engineers to cut out superfluous sections.

For instance, statement coverage analysis can indicate the redundancy of a portion of code if it is left undisturbed during testing for an old feature.

Basic quality indicator:

High statement coverage indicates that a significant percentage of the code has been used during testing, according to the basic quality indicator.

It does demonstrate a level of testing rigor, but it does not ensure software that is bug-free. Achieving 90% statement coverage, for instance, demonstrates a strong testing effort within the software.

Statement Coverage Testing Disadvantages

Concentrate on Quantity Rather than Quality:

Statement coverage assesses code execution but not testing quality. With superficial tests that don’t account for many circumstances, a high coverage percentage may be achieved.

For instance, testing a login system can cover all of the code lines but exclude important checks for invalid passwords.

Ignores Branches and Logic:

When determining statement coverage, conditional structures like if and else statements are ignored.

This could result in inadequate testing of logical assumptions. A software might, for instance, test the “if” portion of an if-else statement but fail to test the “else” portion.

False High Coverage:

Achieving high statement coverage does not imply that the application will be bug-free.

Despite extensive testing, some edge situations or uncommon events might still not be tested.

For instance, a scheduling tool may have excellent statement coverage yet neglect to take into account changes in daylight saving time.

Inability to Capture Input Context:

Statement coverage is unable to capture the context of the input values utilized during testing.

This implies that it might ignore particular inputs that result in particular behavior.

For example, evaluating a shopping cart system might be successful if edge circumstances like negative amounts or large discounts are not taken into account.

The post What is Statement Coverage Testing? appeared first on Testbytes.

This is where White Box Testing comes in, a a powerful process that probes deeply into the inner workings of software to reveal possible faults and vulnerabilities.

By examining its different methods and examples, we will demystify the idea of white box testing in this extensive blog article.

Join us on this trip as we shed light on the many forms of White Box Testing and how they play a critical part in enhancing software quality and security—from comprehending the basic concepts to uncovering its practical implementations.

This article will provide you priceless insights into the realm of White Box Testing, whether you’re an experienced developer or an inquisitive tech enthusiast.

What is White Box Testing With an Example?

White Box Testing is also known by other names such as:

• Clear Box Testing
• Transparent Box Testing
• Glass Box Testing
• Structural Testing
• Code-Based Testing

White Box Testing is a software testing process that includes studying an application’s core structure and logic. It is carried out at the code level, where the tester has access to the source code and is knowledgeable of the internal implementation of the product.

White Box Testing, as opposed to Black Box Testing, which focuses on exterior behavior without knowledge of the underlying workings, tries to guarantee that the code performs as intended and is free of mistakes or vulnerabilities.

This testing method gives useful insights into the application’s quality and helps discover possible areas for development by studying the program’s structure, flow, and pathways.

In white box testing, the tester has to go through the code line by line to ensure that internal operations are executed as per the specification and all internal modules are properly implemented.

Example

Let’s consider a simple example of white box testing for a function that calculates the area of a rectangle:

def calculate_rectangle_area(length, width):
if length <= 0 or width <= 0:
return “Invalid input: Length and width must be positive numbers.”
else:
area = length * width
return area

Now, let’s create some test cases to perform white box testing on this function:

Test Case 1: Valid Input

• Input: length = 5, width = 3
• Expected Output: 15

Test Case 2: Invalid Input (Negative Value)

• Input: length = -2, width = 4
• Expected Output: “Invalid input: Length and width must be positive numbers.”

Test Case 3: Invalid Input (Zero Value)

• Input: length = 0, width = 6
• Expected Output: “Invalid input: Length and width must be positive numbers.”

Test Case 4: Valid Input (Floating-Point Numbers)

• Input: length = 4.5, width = 2.5
• Expected Output: 11.25

Test Case 5: Valid Input (Large Numbers)

• Input: length = 1000, width = 10000
• Expected Output: 10,000,000

In this situation, white box testing entails analyzing the function’s core logic and creating test cases to make sure all code paths are covered.

In order to determine if the function operates as intended in various contexts, test cases are designed to assess both valid and invalid inputs.

White box testing allows us to both confirm that the function is correct and find any possible defects or implementation problems.

White Box Testing Coverage

#1) Code level: Errors at the source code level, such as syntax mistakes, logical mistakes, and poor data handling, are found using white box testing.

#2) Branch and Path Coverage: By making sure that all potential code branches and pathways are checked, this testing strategy helps to spot places where the code doesn’t work as intended.

#3) Integration Issues: White box testing assists in identifying problems that may develop when several code modules are combined, assuring flawless system operation.

#4) Boundary Value Analysis: White box testing exposes flaws that happen at the boundaries of variable ranges, which are often subject to mistakes, by examining boundary conditions.

#5) Performance bottlenecks: By identifying regions of inefficient code and performance bottlenecks, engineers are better able to improve their product.

#6) Security issues: White box testing reveals security issues, such as errors in input validation and possible entry points for unauthorized users.

White box testing, sometimes referred to as clear box testing or structural testing, includes analyzing the software’s core logic and code. This testing technique may be used to find a variety of flaws and problems, including:

Code-level Errors: White box testing uncovers issues at the source code level, such as syntax errors, logical errors, and improper data handling.

Branch and Path Coverage: This testing approach ensures that all possible branches and paths within the code are tested, helping identify areas where the code doesn’t function as intended.

Integration Problems: White box testing aids in detecting issues that may arise when different code modules are integrated, ensuring seamless functioning across the entire system.

Boundary Value Analysis: By exploring boundary conditions, white box testing reveals bugs that occur at the limits of variable ranges, which are often prone to errors.

Performance Bottlenecks: It helps pinpoint performance bottlenecks and areas of inefficient code, allowing developers to optimize the software for better performance.

Security Vulnerabilities: White box testing exposes security vulnerabilities, such as input validation flaws and potential points of unauthorized access.

Difference Between White Box Testing and Black Box Testing

Both of them are two major classifications of software testing. They are very different from each other.

1. White box testing refers to the line by line testing of the code, while black box testing refers to giving the input to the code and validating the output.
2. Black box testing refers to testing the software from a user’s point of view, whereas the White box refers to the testing of the actual code.
3. In Black box testing, testing is not concerned about the internal code, but in WBT testing is based on the internal code.
4. Both the developers and testers use white box testing. It helps them validate the proper working of every line of the code.

Steps to Perform White Box Testing

Understand the Source Code:

A thorough knowledge of the source code is essential before beginning white box testing. To do this, examine the code base and note important modules, functions, and data structures. You may efficiently develop test cases that focus on particular aspects of the application by becoming familiar with the code.

Create Test Cases:

Create Test Cases: The foundation of white box testing is the creation of thorough test cases. Create test cases that cover numerous pathways, situations, and scenarios based on your understanding of the code. Your testing efforts will be guided by these test cases, ensuring that every facet of the code is carefully inspected.

When identifying test cases, you should consider the following:

* All possible paths through the code
* All boundary conditions (e.g., minimum and maximum values)
* All error conditions (e.g., invalid inputs)

Choose Testing Techniques:

A variety of testing methods are used in white box testing to evaluate code coverage. Statement coverage, branch coverage, path coverage, and condition coverage are some of these strategies. Select the right approaches to direct your testing strategy based on the amount of thoroughness necessary.

Perform Unit Testing:

To begin, conduct unit testing, which entails testing distinct components or modules. To verify their accuracy and find any defects or problems, each function or method should be evaluated using several inputs. The basis for guaranteeing the dependability of each code component is laid out through unit testing.

Execute Integration Testing:

Integration testing aims to evaluate the relationships between various components. Test for compatibility, data flow, and interactions when components are merged. This stage is essential for finding integration problems and making sure the application runs well.

Apply Coverage Analysis:

White box testing relies heavily on coverage analysis. To determine how much of the code your test cases cover, use coverage analysis tools. This gives you information about parts of the code that your tests do not cover, enabling you to improve coverage and reduce blind spots.

Use Boundary Value Analysis:

Use boundary value analysis to test the application with values at the outside limits of the input ranges. You can find flaws or problems relating to edge situations by testing extreme values. By doing this, you can make sure the program will function properly in many scenarios.

Implement Decision Testing:

Decision testing is designed to assess multiple outcomes of decision points—such as if-else and switch cases—within the code. Make careful you properly test each conceivable branch of the decision-making logic to identify any potential flaws or inconsistencies.

Evaluate Data Flow:

Analyze the data flow within the code to evaluate it. To guarantee data integrity and correct handling, test situations where data is transmitted across several code sections. This process is crucial for locating problems with data modification and transmission.

Run Regression Tests:

Once defects have been located and fixed, run tests to make sure the changes made did not result in any new problems or alter functionality. Regression testing protects against unforeseen effects of modifying the code.

Perform Code Reviews:

Work with developers and other team members to evaluate code. You may find problems early in the development process and improve overall code quality by exchanging ideas and feedback. Code reviews encourage group learning and development.

Document Results:

Results must be meticulously documented at every stage of the white box testing procedure. Record the test cases that were run, the level of coverage attained, the problems found, and the remedies implemented. This documentation offers a detailed account of your testing activities and results.

Iterate and Refine:

White box testing is an iterative process, so iterate and refine as necessary. Review your test cases, make any necessary updates, and carry out more testing as necessary when the code changes or evolves. The robustness and resilience of the code are maintained through this iterative process.

Collaborate:

Throughout the white box testing process, effective communication and collaboration with the development team are essential. Exchange ideas, disseminate research, and collaborate to raise the caliber and usefulness of the codebase.

Types of White Box Testing/Techniques used in White Box Testing

The term “white box testing,” also known as “clear box testing” or “structural testing,” refers to a variety of testing methods, each with a particular emphasis on a distinct element of the core logic and code of the product. The primary categories of White Box Testing are as follows:

Statement coverage testing

During the testing process, this approach seeks to test every statement in the source code at least once.

Branch coverage testing

Testing for branches or decision points is known as branch coverage, and it makes sure that every branch or decision point in the code is tested for both true and false outcomes.

Path coverage testing

Path coverage testing examines all potential statements or statement combinations in the code to ensure thorough code execution.

Condition coverage testing

The goal of condition coverage testing, commonly referred to as “decision coverage,” is to make sure that all potential outcomes of boolean conditions inside the code are evaluated at least once. This method aids in ensuring that every choice or branch in the code gets executed on its own.

Example:

Loop Coverage Testing

Testing loops in the code to make sure all conceivable iterations are carried out is the subject of the loop coverage testing approach.

Let’s consider an example of loop coverage testing using a simple program that calculates the factorial of a given number using a for loop:

In this example, the ‘factorial’ function calculates the factorial of a given number using a ‘for’ loop. Loop coverage testing aims to test different aspects of loop behavior. Here are the scenarios covered:

Test case 1: Calculating the factorial of 5. The loop runs from 1 to 5, multiplying result by 1, 2, 3, 4, and 5. The expected result is 120.

Test case 2: Calculating the factorial of 3. The loop runs from 1 to 3, multiplying result by 1, 2, and 3. The expected result is 6.

Test case 3: Calculating the factorial of 0. Since the loop’s range is from 1 to 0+1, the loop doesn’t execute, and the function directly returns 1.

Boundary Value Analysis

It evaluates how the program behaves at the border between acceptable and unacceptable input ranges.

Let’s consider an example of a function that calculates the cost of shipping a package based on its weight:

Data flow testing

Data flow testing looks at how data moves through the program and confirms that data variables are handled correctly.

Control flow testing:

Control flow testing analyzes the order of the statements in the code or the control flow.

Let’s consider a simple example of a control flow testing scenario using a function that calculates the discount for a shopping cart based on the total amount spent:

In this example, the ‘calculate_discount’ function calculates the discount based on the total amount spent. Control flow testing aims to test different paths through the function’s code, including the if-elif-else branches. Here are the scenarios covered:

Test case 1: The total amount is 150. It satisfies the condition total_amount >= 100, so the discount should be 0.1 (10%).

Test case 2: The total amount is 80. It doesn’t satisfy the first condition but satisfies the second condition total_amount >= 50. Hence, the discount should be 0.05 (5%).

Test case 3: The total amount is 30. It doesn’t meet any of the conditions, so the discount remains 0 (no discount).

Testing using Decision Tables

Based on predetermined criteria, decision tables are used to test different combinations of inputs and their related outputs.

Let’s consider an example of a decision table for determining the eligibility of a loan based on income and credit score:

Example: Loan Eligibility Decision Table

In this example, the decision table has four conditions: “Income Range,” “Credit Score Range,” and “Eligibility.” The different possible values for each condition are represented in rows. The “Eligibility” column indicates whether the applicant is eligible for a loan based on the given conditions.

For instance, if an applicant has a high income and a good credit score, they are eligible for a loan according to the decision table (C7). Conversely, if an applicant has a low income and a poor credit score, they are not eligible (C3).

Testing using a decision table involves creating test cases that cover all the different combinations of conditions and their expected outcomes. Here are a few example test cases:

1. Applicant with low income and good credit score:

• Income Range: Low
• Credit Score Range: Good
• Expected Eligibility: Not Eligible

2. Applicant with moderate income and fair credit score:

• Income Range: Moderate
• Credit Score Range: Fair
• Expected Eligibility: Eligible

3. Applicant with high income and poor credit score:

• Income Range: High
• Credit Score Range: Poor
• Expected Eligibility: Not Eligible

By systematically testing each combination of conditions based on the decision table, we can ensure that the application behaves as expected for different scenarios and helps in verifying that the defined rules are correctly implemented.

Mutation Testing

In order to determine how well the test suite is able to identify these alterations, mutation testing includes making minor modifications or mutations to the code.

These numerous White Box Testing approaches are used to test the underlying logic of the program thoroughly and achieve varying degrees of code coverage. Depending on the complexity of the product and the testing goals, testers may combine various approaches.

Top White Box Testing Tools

#1) Veracode

Veracode is a prominent toolkit that helps in identifying and resolving the defects quickly, economically and easily. It supports various programming languages like .NET, C++, JAVA, etc. It also supports security testing.

#2) EclEmma

EclEmma is a free Java code coverage tool. It has various features that ease the testing process. It is widely used by the testers to conduct white box testing on their code.

#3) JUnit

JUnit is a widely-used testing framework for Java that plays a crucial role in automating and simplifying the process of unit testing. It provides a platform for developers to write test cases and verify their Java code’s functionality at the unit level. JUnit follows the principles of test-driven development (TDD), where test cases are written before the actual code implementation.

#4) CppUnit:

CppUnit is a testing framework for C++ that was created to facilitate unit testing for C++ programs. It is based on the design concepts of JUnit. It allows programmers to create and run test cases to verify the accuracy of their C++ code.

#5) Pytest

This C++ test framework by Google has an extensive list of features including test Discovery, Death tests, Value-parameterized tests, fatal & non-fatal failures, XML test report generation, etc. It supports various platforms like Linux, Windows, Symbian, Mac OS X, etc.

Advantages of White Box Testing

• Code optimization
• Transparency of the internal coding structure
• Thorough testing by covering all possible paths of a code
• Introspection of the code by the programmers
• Easy test case automation

Disadvantages of White Box Testing

• A complex and expensive procedure
• Frequent updating of the test script is required whenever changer happens in the code
• Exhaustive testing for large-sized application
• Not always possible to test all the conditions.
• Need to create a full range of inputs making it a very time-consuming process

Conclusion

White box testing is a predominantly used software testing technique. It is based on evaluating the code to test which line of the code is causing the error. The process requires good programming language skills and is generally carried out by both developers and testers.

FAQs

#1) How can developers ensure adequate code coverage when performing White Box Testing?

By using a variety of techniques, developers may guarantee proper code coverage in White Box Testing.

They should start by clearly defining test goals and requirements, making sure that all crucial features are included. It is best to write thorough test cases that cover all potential outcomes, including boundary values and error handling.

To track the amount of code that is exercised by tests, code coverage tools like Jacoco or Cobertura may be utilized. It is crucial to remedy low-coverage regions by adding test cases or making adjustments after regularly analyzing code coverage metrics to do so.

To carry out thorough testing effectively, test automation should be used, and branch and path coverage guarantees that all potential choices and code routes are checked.

Working together with the QA team ensures thorough integration of White Box and Black Box Testing. Developers may improve code quality and lower the chance of discovering bugs by adhering to certain best practices.

#2) What are some best practices for conducting effective White Box Testing in a development team?

effective White Box research Following various best practices is necessary while testing in a development team. Here are a few crucial ones:

Clear needs: As this information informs test case creation, make sure that the team has a complete grasp of the functional and non-functional needs for the project.

Comprehensive Test Cases: Create detailed test cases that cover all possible code pathways, decision points, and boundary conditions. This will guarantee complete code coverage.

Code reviews: It should be conducted on a regular basis to guarantee code quality, spot possible problems, and confirm that tests are consistent with code changes.

Test Automation: Use test automation to run tests quickly and reliably, giving you more time for exploratory testing and a lower risk of human mistake.

Continuous Integration: Include testing in the process to spot problems before they become serious and to encourage routine code testing.

Test Data Management: To achieve consistent and reproducible test findings, test data should be handled with care.

Code coverage : metrics should be regularly monitored in order to identify places with poor coverage and focus testing efforts there.

Collaboration with QA Team: FCollaboration with QA Team: Encourage cooperation between the QA team and the developers to make sure that all White Box and Black Box Testing activities are coordinated and thorough.

Regression testing: Regression testing should be continuously carried out to ensure that new code modifications do not cause regressions or affect working functionality.

Documentation: Test cases, processes, and results should all be well documented in order to encourage team collaboration and knowledge sharing.

The post What is White Box Testing? Techniques, Examples and Types appeared first on Testbytes.

How does Artificial Intelligence work?

• AI works by merging big data sets with iterative processing algorithms and with intelligence to learn from features and patterns in the dataset that AI systems Analyze.
• Each time an AI system performs a data cycle, it checks and measures its performance and gains additional knowledge.
• AI science aims to create computer systems that can simulate human behaviour to solve complex problems using human-like thought processes.
• AI systems use different technologies and different methods and processes to achieve this goal.

AI helps to access and manage the computing resources to train, test, and deploy AI algorithms and is playing a essential role in the software industry which also includes software testing. Testing is the basic activity aimed at detecting and solving technical issues in the software source code and assessing the overall product usability, performance, security, and compatibility. It’s not only the main part of quality assurance; it is also an integral part of the software development process.

Since AI has the ability of mimicking human intelligence, the penetration of AI in testing is on the rise.

Evolution of AI in Software Testing

According to the World Quality Report 2019-2020, it is stated that AI-based testing is on the rise, and to make testing smarter, more effective, and more efficient, organizations are adopting AI-based tooling and processes in software testing. Typically, the application of AI in software testing will make the entire testing process faster, clearer, easier, and budgeted. Therefore, AI-based testing will provide a strategic platform where software testers can leverage AI and take the testing process to a new level and thus deliver more quality results to businesses.

The paradigm of software testing has evolved significantly over the past two decades. Right from manual testing to automation testing, where selenium is considered to be one of the finest test automation tools, the testing journey has been quite encouraging. However, in today’s fast-paced IT world, the domain of software testing has to come up with innovative and well-researched testing methodologies.

AI algorithms can completely mimic human intelligence, and ML allows computers to learn automatically without any human intervention. Interestingly, AI and ML involve the development of unique and specific algorithms that can access data learn from that data by extracting patterns to make decisions, and these predictions are to be used in software testing effectively.

Read Also: Personality Analysis of Software Testers A Scientific Approach

Benefits of AI in Software Testing

1. Reduced Test Flakiness
By automating repetitive tasks and using algorithms to detect bugs and issues, AI helps to speed up the testing process and improve the accuracy of results. This means all the software can be tested more efficiently and effectively, saving time and resources while ensuring a higher-quality product.

2. Better Test Coverage
Artificial intelligence has the potential to automate manual tests and identify issues quickly, reducing the time required to detect bugs and errors. By automating testing activities and reducing human error, AI can help you deliver better quality software more quickly.

3. Faster Feedback Time
AI helps in faster detection of bugs in the product. Early detection results in improved product quality since the developers receive faster feedback about the product. Accelerated feedback time also improves developer productivity since issues are reported at a faster pace. The impact of AI-based testing multiplies by a huge margin when the tests are run in Continuous Integration pipeline.

4. Faster Test Script Generation
Codeless AI testing is significantly faster than either manual testing or traditional automated solutions, as testers save time generating code. This allows companies to increase their ability to run tests and deploy more quickly.

What is AI-based Testing?

AI-based testing is a software testing technique in which AI and Machine Learning (ML) algorithms are used to effectively test a software product.

Machine learning is one of the key techniques we use to achieve this. It forms the basis for many AI systems, but not all. AI and machine learning in software testing deliver better and more effective automation, relieving teams of the burden of repeating and refining testing.

Many software testing methods are now powered by Artificial Intelligence .The objective of AI-based testing is to make the testing process smarter and highly effective. With the inclusion of AI and ML in testing, logical reasoning and problem-solving methods can be applied to improve the overall testing process.

Moreover, enterprises are rushing towards tools that can leverage AI and ML algorithms and can be used for testing the software effectively. It has also been seen that businesses get many benefits from AI-based testing as it will enable faster and continuous testing, complete automation without any human intervention.

Read Also: AI and Bots: Are They The Future of Software Testing?

Some of the benefits of leveraging AI in software testing:

1. Visual validation:–

It helps to make sure that all the visual elements are engaging and can function properly. Improved accuracy: with the advent of AI in automation testing, repetitive tasks are handled more effectively and the results are recorded more accurately. AI has pattern recognition and image recognition capabilities that together help to detect visual bugs by performing visual testing on applications. It helps to make sure that all the visual elements are engaging and can function properly.

2. Improved accuracy:–                                                                                 

Through machine learning, AI helps to generate test data where testers can feed the data into an AI machine allowing it to perform various tests at every stage without the need for manual testing hence improving the reliability and security of the software.

In the manual testing method, the chances of human-prone errors are high, especially in situations of repetitive tasks. Automation testing helps in removing these human-prone errors. Thus, AI helps in removing the minute chances of errors and improves the overall accuracy of tests.

3. Better test coverage:–

AI in testing increases the test coverage as it can check the file contents, data tables, memories, and internal program states seamlessly. Saves time, money, and efforts: Software tests need to be repeated whenever there is an instance of change being made in the source code. AI in testing increases the test coverage as it can check the file contents, data tables, memories, and internal program states seamlessly. It also helps to determine if the program works as expected and delivers effective test coverage.

4. Saves time, money, and efforts:-

Software tests need to be repeated whenever there is an instance of change being made in the source code. Manually this becomes time-consuming and takes a lot of effort from testers. But, with AI-driven tests, repetitive tasks are handled properly, quickly, and efficiently.

5. Faster time-to-market:–

AI uses a set of algorithms to analyze software functions and identify errors through automated testing, thus minimizing the headaches of repetitive software testing tasks (such as regression tests), improving accuracy, and accordingly shortening time to market. AI-driven tests support continuous testing, and thus products are released faster which helps businesses go early-to-market.

6. Reduces defects:–

AI-driven tests support continuous testing, and thus products are released faster which helps businesses go early-to-market. AI in testing helps in early and fast bug identification, which ultimately reduces the defects and makes the product bug-free, and reliable for end-users.

What are the 4 main categories of AI-driven testing tools?

1. Differential tools:-

Helps test different versions of similar applications. Carries out a comparison to understand differences, versions overbuilds and learn from classification feedback. Visual: Image-based testing needs visual validation. Differences are classified and application versions over each build are compared in this type of testing.

Tools leveraging AI and ML algorithms aim to proactively and automatically identify code quality issues, regressions, security vulnerabilities, and more. This is done through code scanning, unit test automated creations, and more. If your team lacks skills to address the above objectives or does not have the time to continuously address these tasks, consider some of these options. The outcome will be faster releases, improved quality through fewer escaped defects, and better productivity for developers. Some of the tools under this category are:

• Launchable:

Launchable is based on an ML algorithm that predicts the likelihood of failure for each test based on past runs and whenever the source code changes under test. This tool lets the user record the test suite so that tests that are likely to fail are run first. One can choose this tool to run a dynamic subset of tests that are likely to fail, thereby reducing a long-running test suite to a few minutes.

It looks at code automatically upon a code pull request and performs a kind of code impact analysis that adapts to the recent code changes. It then selects only the most relevant subset of your regression suite to save time to approve the code changes and integrate them into the pipeline.

•  Google OSS-Fuzz:

It is a fuzz testing tool that aims to make common open-source software more secure, stable, and reliable. This tool combines modern fuzzing techniques with scalable and distributed execution. This tool supports C/C++, Rust, Go, and Python code.

• Facebook Infer:

Facebook’s Infer project also enables better code quality through its AI algorithm. The AI engine from Facebook can automatically find null pointer exceptions, memory leaks, concurrency race conditions, and more in Android and Java code. Similarly, it can also find the same issues together with wrong coding conventions or unavailable APIs in C, C++, and iOS/Objective C code.

• DiffBlue:

DiffBlue connects into your source control repository (Git, Perforce, etc.) and creates a base line of unit testing automatically through AI. Once a regression is found, a flag will be thrown reporting the issue. The motivation for DiffBlue to create their solution was mostly to improve code quality by helping developers who do not like to own test creation.

Read Also: Software Testing Latest Trends & Technology in 2023

2. Visual AI testing tools

Visual testing is a software testing technique in which the look and feel of an application is tested by leveraging image-based learning and screen comparisons. With pattern and image recognition capabilities together, it helps detect visual bugs to test the look and feel of an application.

Visual AI testing tools address the pain of constant changes made to the UI (user Interface) layer together with an ever-growing number of platforms, screen sizes, and configurations that make testing coverage a nightmare for test engineers and developers. With the ever-growing number of platforms that vary in screen sizes and have different configurations, it has become a tedious task for test engineers and developers to effectively test the UI layer.

Also, the UI layer experiences constant changes from time-to-time as businesses wish to provide a better user experience. Therefore, today there is a dire need for visual AI testing tools that effectively test all variations of these UI layers.

Some AI/ML tools that fall into this category are:

• Applitools
• Percy.io

1. Applitools:

This is an AI-powered visual testing and monitoring platform. This has been named a next-generation test automation platform powered by Visual AI. The major features include Applitools Eyes which helps to increase test coverage and reduce maintenance. The Ultrafast grid helps with cross-browser and cross-device testing and accelerates functional and visual testing by 30 times. This Applitools platform integrates with all modern test frameworks and works with many existing testing tools like Selenium, Appium, Cypress, etc.

2. Percy by BrowserStack:

It is an all-in-one visual review platform that comes with amazing features such as pixel-by-pixel diffs, responsive diffs, and snapshot stabilization. This tool allows cross-browser rendering, high-speed rendering, and has parallelization capabilities. Percy helps teams automate visual testing. This Browserstack tool is used to typically capture screenshots and compare them against the baselines and display visual changes. It increases the visual coverage and helps teams to deploy code changes with confidence.

3. Declarative tools

Declarative tools have different use cases from the others but still aim to enhance test automation productivity and stability. Declarative tools that leverage ML and AI have significant abilities related to NLP, DSL, RPA, and MBTA methods. The common ground between the methods is to eliminate tedious, error-prone, repetitive actions through smart automation. While in this category we list RPA, this specific method is not solely around automation of testing, but also around automation of processes and tasks done manually.

These tools aim to enhance test automation productivity and stability. These tools leverage AI and ML and have significant abilities related to Robotic Process Automation (RPA), Natural Language Processing (NLP), Model-based Test Automation (MBTA), and Autonomous Testing Methods (AT). The main aim of these methods is to eliminate tedious, error-prone, repetitive tasks through smart automation. Some of the tools that fall under this category are:

Focusing on declarative testing, we can take as an example tools like:

• Functionize
• Tricentis
• UIPath Test Suite
• Automation Anywhere

1. Functionize:
Especially Functionize, specify leveraging NLP to create test automation scripts without any coding skills or development languages.

The major benefits of this tool type are as follows

Fast test automation creation.
No coding skills are required.                                                                                    Faster maintenance of test automation scenarios.

2. Tricentis:
This is an AI-driven, next-gen automation testing tool that allows Agile and DevOps teams to rapidly achieve test automation goals. It allows teams to go beyond continuous testing with AI. It allows automating end-to-end testing of software applications. This tool combines multiple aspects of software testing (test case design, test automation, test data design and generation, and analytics) to test GUIs and APIs from a business perspective.

3. UiPath Test Suite:
This is the latest Test Suite that can be used to automate and centralize the testing process and helps to launch resilient robots and ensures high-quality of every automation. The UiPath Test Suite consists of UiPath Studio Pro, UiPath Test Manager, and UiPath Orchestrator. Thus, UiPath test Suite can be used to automate tests in UiPath Studio Pro with drag and drop interfaces, helps to manage tests with UiPath Test Manager, and also helps to execute tests witn UiPath Orchestrator. Therefore, UiPath Test Suite is helping businesses with a 360 degree testing and is helping RPA developers to build more, test better, and fix never.

4. Automation Anywhere:
These types of tools should solve problems for the right persona depending on the skillset available.

4. Self-healing tools

Apply AI to testing to identify when a function has changed. Then, the test can automatically update itself to be relevant and not fail during execution. Element selection in tests is auto-corrected when the UI changes.

Code-based test automation is by nature less stable. It requires tuning constantly per platform or environment, and its entire foundation is the application objects. These objects tend to either change every few weeks, or worst case they are used inefficiently (e.g. XPATH vs. Object ID, etc.).

Some of the tools are as simple as a web browser plugin installation (Mabl, Testim). Some tools that assist in test maintenance with machine learning are richer in their abilities and are integrated into an end-to-end continuous testing solution (Perfecto, Tricentis).

• Perfecto
• Mabl

At the heart of these tools there is a ML algorithm that upon each execution and in between them “learns” the website and/or application under test. It scores the element locators from each screen in the app based on reliability and probability to be found successfully.

Code-based test automation is by nature less stable. It requires tuning constantly per platform or environment, and its entire foundation is the application objects. These objects tend to either change every few weeks, or worst case they are used inefficiently (e.g. XPATH vs. Object ID, etc.).

In automation tests, the problem of flakiness, reliability, and maintenance issues persist, and this is one of the main reasons why AI and ML have been introduced in test automation. To overcome these problems, self-healing tools have been developed that are mostly based on a record and playback mechanism, wherein the main ML engine resides in the self-healing of the recorded scripts. Some of the tools that fall under this category are:

• Mabl:
  It is the leading intelligent test automation platform built for CI/CD. Mabl crawls your app screens and begins to run default tests that are common for most applications. It also uses ML algorithms to improve test execution and defect detection.
• Testim:
  This tool uses AI and ML algorithms to automate testing to its full extent. AI is used to speed up the authoring, execution, and maintenance of the tests. Testim includes self-maintenance of automated tests that are ML-based. This results in the fast authoring of stable automated tests.

Best Practices for Testing AI Applications:

Assess Information Technology infrastructure

Successful execution of an AI strategy requires discipline and the best practices listed here. Responses may also contribute to adoption. Consider the use of resources in terms of the time, cost, complexity, and skill set required to build an AI model and demonstrate a business case.

Determine the use cases

Determine how the peers and competitors have strongly deployed AI platforms. Look for suppliers with a solid track record to mitigate risk. They talk to stakeholders about the utilization cases and the benefits of implementing AI.

Also, use AI accelerators from popular cloud service providers (CSPs), which may already be included in BPM (Business Process Management), RPA (Robotic Process Management), DMS (Document Management System), and iPaas (Integration Platform as a Services) platforms. Working with stakeholders and educating them on how to use AI solutions increases their likelihood of use and drives adoption across the organization.

Search for relevant use cases for the optimized deployment of artificial intelligence in each of the following areas:

• Machine learning (ML)
• Natural language processing (NLP)
• Natural language understanding (NLU)
• Optical character recognition (OCR)
• Chatbots

Learn how your competitors and peers have successfully deployed AI platforms. Look for vendors with a reliable track record to reduce risk. Consult with stakeholders on your use cases and the advantages of implementing AI.  Also, leverage AI accelerators from prominent cloud service providers (CSPs) that may already be included within your LCAP, DMS, BPM, RPA, and iPaaS platforms. By working with your stakeholders and teaching them how to use your AI solution, the more likely they are to use it, driving organization-wide adoption.

Understand the Raw Data

Insufficient data may lead to misrepresented results and AI implementation failure. If you can comprehend the raw data, garner your business experts’ assistance to access a detailed interpretation. Comb through the data to ensure there aren’t any typos, missing components, skewed labels, and other errors. Ensure your data samples contain every element you need to analyze. Incomplete data may cause misleading represented results and AI execution failure. Ensure that the sample data contains all the elements required for analysis.

Losing focus on the raw data can lead to skewed results and loss of confidence on the machine learning models. If you do not understand the data, get help from business experts to gain a full understanding of the story the raw data is telling you. Analyze it to ensure that there are no missing values, incorrect labels, or typos and check that the sample contains the full spectrum of all users that you wish to analyze.

Also, consider the relationship between data labels and values that you are trying to predict based on dependent data and ensure that there is no biased data (data favoring a particular result). While analyzing the raw data, you will get an understanding of the limitations of your data set and model. This will help you communicate the scope and limitations of your predictions based on the pattern of the data to your stakeholders.

Train the models

You will need high-quality historical data to train your ML models. Use AutoML engines to build image, speech, video, and natural language, recognition models. With AutoML engines, any user can upload their images and automatically create an ML model using a drag-and-drop interface. Essentially, it imports data, tags the data, and trains the model. The best part is that an AutoML engine manages all the complicated work for you.

Training an ML model requires high-quality historical data. Generate natural language recognition, image, video, and speech using the Auto Machine Learning engine (AutoML). The AutoML engine allows users to upload images and automatically generate ML models using a drag-and-drop interface. Import the data, label the data and train the model. The best part is that the AutoML engine takes care of all the hard work.

Measure and record the results

You should experiment with artificial intelligence, but you should also incorporate disciplined tracking, monitoring, and measurement at every step using a critical approach. Also, it’s essential to continually audit your deployment to ensure it consistently aligns with your business objectives. Changing your strategy is more effective than accepting failure.

Continue testing your models and predictions to drive further improvements where necessary. Keep your data clean, and retain a master raw data set to use for every testing round. You can also use your master data set to test modified use cases. Monitor your model for potential risks and issues. Don’t forget to add time for managing any unexpected problems. While performing AI tests, one should also incorporate measurement, precise tracking, and monitoring using a complex approach throughout the action.

Also, it is essential to continuously check the deployment to ensure it is frequently coordinated with the business objectives.

Guide the team and cooperate

Artificial intelligence continues to get better, but it still requires the correct data. The issue is it’s difficult to find data science experts. Therefore, invest in continuing education for your stakeholders. Add to your training initiatives by creating an environment where collaboration is part of the culture. A crucial factor for AI implementation success is change management.

Create short-term and long-term objectives of what you expect to achieve using predictive analytics and then machine learning and then natural language processing and on down the AI list. Map out how each deployment affects each business line and how it enhances your employee workflows AI continues to improve, but it still needs relevant data. The problem is that it is difficult to find data science experts. Therefore, invest in further participatory education.

Admit all the wins

Celebrate every win, and involve every executive and stakeholder. Try to complete your projects within or before 12 weeks to encourage continued engagement. As you learn from each successful project, you can scale AI across more business lines and company locations. Use your goals as success benchmarks, and focus on your results. When focusing on the outcome, keep in mind that AI platforms can take structured and unstructured data sets.

Finally, using best practices for implementing AI requires a long-term perspective. Remember that AI deployment is a marathon and not a spring. Understand what AI is currently capable of executing, and be realistic about your timelines and your expectations.

Conclusion:-

A better understanding of the differences between AI and human intelligence is needed to better prepare for the future in which AI will have the most profound effect on our lives. With the advent of AI in software testing, businesses are now able to achieve faster tests and reliable products. Leverage next-gen AI-based testing services by next-gen testing services provider to get faster and quality releases with more efficiency and accuracy.

There are best practices for implementing AI in companies like Assessing IT infrastructure, determining the use cases, understanding the data, training, and measuring the records. An AI application needs to be tested for functionality and system levels. It is similar to testing of traditional software in aspects of test planning, test modelling, test design, and execution. Testing of an AI system becomes more challenging and function test quality evaluation becomes an integral part of AI application testing.

The post Strategies for Testing in AI Applications Best Practices and Key Considerations appeared first on Testbytes.

So much to an extent where the role of the tester has completely changed! With the coming in of the PDO (Product Driven Organization) structure, there are no more testers and developers but only full-stack engineers.

Bottom line is testing still needs to be done.

Who does that? How does it fit in the 2-week agile sprint? Is manual testing even possible in such a short time?

The Answer

To start with, the scope for manual testing has reduced. Agree to it or not. This is what happens in real-life scenarios. Since testing is still a task on our User Stories, it needs to be completed. Most teams take the help of automation tools.

Now here is the challenge, many small and even big companies are going to the open-source automation tools which give them the flexibility to customize as per their need without any investment.

There are several tools available for you to choose based on the kind of application you have like a web-based app or a mobile app or a desktop software etc.

Selenium

Selenium has established itself as the most popular web browser automation tool with support for most of the available browsers including Chrome, Mozilla, Opera, and Safari, etc. with equal ease and flexibility. It also supports scripting in several languages including Java, Python, and C#.

This enables even developers to write create and execute automation test cases through Selenium.

This is also an open-source tool that makes it more acceptable to companies.

This tool has become so popular because of the flexibility and controls it offers in execution the commands on the browser.

Multiple browser support means that we can use it for cross-browser testing as well.

There are several other advantages as well when it comes to Selenium.

Puppeteer

When it comes to web automation the most commonly used browser is Chrome which is from Google.

Now Puppeteer is a node.js library from Google as well, which gives you a high-level API control over Chrome and Chromium using the DevTools protocol. In simpler terms, being from the same parent gives it greater control and flexibility to work with Chrome.

With Puppeteer you can take a deep dive into Chrome functionalities easily which could be a challenge when it comes to other open-source automation tools.

Chai Mocha

Chai and Mocha are JavaScript frameworks that are commonly used together. Mocha is a simple testing framework that will allow you to execute a set of functions in sequence.

The results of these tests are displayed on the terminal window. While Chai is an assertion library that is used to validate the test case results.

By using Chai with Mocha the user can get the results in the required format.

This Chai Mocha framework is frequently used for unit testing by developers to perform tests and report results.

Hope you have got a basic understanding of these tools. Now let us see some comparative data for these 3 tools, to help you understand which one to choose for your need.

Let us see how the tools discussed here can help you with your testing tasks.

Hope this data comparison is helpful for you to decide which one to pick up for your team and project. My suggestion, if you are dealing with only Chrome then goes for Puppeteer.

But if you want your application to run across all platforms and you want it to be tested in multiple browsers and platforms Selenium would be the right choice.

With Selenium, the coding and tool expertise required is also limited, which means you can build up your team and competency faster.

So our personal choice is Selenium which offers more features and online support forums for guidance as well.
Take your pick.

The post Selenium vs Puppeteer vs Chai Mocha appeared first on Testbytes.

We provide introductory information about the  well as the source of the research papers for software testing, which is either free or paid.

There are very few libraries in the research area where research papers are accessible; some papers are protected by their authors; we can access the research paper by requesting or paying the appropriate amount. The impact of technology on our businesses is exciting and the opportunities for us are unlimited.

Here are some major trends for research parameters that are changing the face of software testing

1. Springer Nature:

Our very first platform that is useful for researchers is “Springer Nature”. Springer Nature is a publishing, educational, and research-based company. The company looks to provide resources primarily for researchers and scientists.

Springer Nature advances discovery by publishing trusted research, supporting the development of new ideas and championing open science. We are committed to playing our part in accelerating solutions to address the world’s urgent challenges.

There are some journals published by Springer that are specifically designed for software testers’ research. “Software Quality Journal“  and “Automated Software Engineering” are the journals where researchers can publish their research as well as some past researchers’ papers for reference. These journals are generally free to readers, but some papers are protected by the author; users can access the references after requesting access or paying access fees. For access to the Springer Nature Library, follow the link https://www.springernature.com/gp

Read Also: Personality Analysis of Software Testers A Scientific Approach

2. Clarivate

Web of Science is the world’s most powerful research engine, which is a subscription platform that gives access to many databases including reference and citation data from academic journals, conference proceedings, and other materials in a variety of academic subjects.

It was created by the Institute for Scientific Information. It is utilized for searching of a subject and cited references; for instance, it retrieves the articles that are cited by a reference article and also helps in the viewing of the references that are already cited in a relevant article.

Clarivate is the current owner which helps you collect and analyze information for insights you can easily act on. Under this, we can get the papers based on title or keywords. Based on software testing, the platform almost covers the majority of research for access to the Web of Science Library by using this link https://mjl.clarivate.com/search-results.

3. Mendeley

“Mendeley” is a reference management programme that was established in 2007 by PhD students Paul Foeckler, Victor Henning, and Jan Reichelt and was purchased by Elsevier in 2013. It is used to organise and distribute research papers, as well as to produce bibliographies for scholarly works. Following the purchase, the Mendeley team expanded its product portfolio while iterating on its core reference management application. Mendeley can track reader numbers, which have been shown to predict citation impact, although journal-level metrics are poor indicators of dependability.

It covers a large dataset about software tester research publication for more explosions Login in Mendeley and access the trending subject as references. Mendeley is a free reference manager that can help you collect references, organize your citations, and create bibliographies which manage the software that helps to manage your research data in systematic way. Below is the link which helps the researcher easily to enter in mendeley https://www.mendeley.com/?interaction_required=true

Read Also: History of Software Testing Estimation Models for Cost Analysis

4. ResearchGate

“ResearchGate” is a commercial social networking service for academics and researchers around the world that allows them to exchange papers, ask and answer questions, and locate partners i. e. it discover scientific knowledge and stay connected to the world of science While viewing articles does not necessitate registration, site members must have an email address from a recognised university or be personally certified as published researchers.

The research gate shows the result based on the title and the keywords under the banner of ResearchGate, no specific journal is designed for software testing. For reference purposes, it covers a wide range of papers in the software testing domain and getting the papers based on the title and keywords do follow the following link https://www.researchgate.net/.

5. Institute of Electrical and Electronics Engineers (IEEE)

The “Institute of Electrical and Electronics Engineers (IEEE)” is a professional society for electronic and electrical engineering, which is an organization dedicated to advancing innovation and technological excellence for the benefit of humanity, is the world’s largest technical professional society. It is designed to serve professionals involved in all aspects of the electrical, electronic, and computing fields and related areas of science and technology that underlie modern civilization. The IEEE’s aim is to advance technology for the benefit of humanity.

The IEEE has some journals that are very useful for the domain of software testing. The journals are as follows: “IEEE Access”, “IEEE Transactions on Software Engineering,” and “IEEE Software” are the three most popular journals that are useful for software tester research aspirants and there is IEEE Standard for Software Test Documentation which is a set of basic test documents that are associated with the dynamic aspects of software testing that is, the execution of procedures and code.

The role of IEEE in current technology is to advance computer and information processing science and technology; promote professional interaction; and keep members up-to-date on the latest developments.  If you want to publish your article or need some standard papers for reference, go to IEEE and access them; some are free and some are subscription-based. We are providing the link for easy access https://ieeexplore.ieee.org/Xplore/home.jsp

6. Elsevier

“Elsevier” is a scientific, technical, and medical Publishing Corporation which takes a hand in shaping the future of knowledge with roles dedicated to delivering innovations and improvements to our platforms. Elsevier has Agile work flows embedded within the company which utilizes cutting-edge technological practices and focused on improving your technical skills, and they provide access to resources to help you progress.

Electronic and print editions of periodicals, text books, and reference works covering the health, life, physical, and social sciences are among the products and services offered. Academic and government research institutions, corporate research labs, booksellers, librarians, scientific researchers, authors, editors, physicians, nurses, allied health professionals, medical and nursing students and schools, medical researchers, pharmaceutical companies, hospitals, and research establishments are among the target markets. Following are the some standard Elsevier journals in the software testing domain:

1. “Security Controls Evaluation, Testing, and Assessment Handbook.”
2. “SDL ’97: Time for Testing”
3. “Practical Model-Based Testing”
4. “Usability Testing Essentials”:
For direct access to the Elsevier go through the link https://www.elsevier.com/en-in

Read Also: Software Testing Latest Trends & Technology in 2023

7. Semantic Scholar

“Semantic Scholar” is an artificial intelligence-powered scientific literature research tool created at the Allen Institute for AI and made public in November 2015. It makes use of improvements in natural language processing to produce scientific article summaries. Semantic Scholar is examined to locate primary papers of selected secondary studies and identify missing venues.

The proposed search strategy is used to check the ability to retrieve primary papers of each secondary study. The Semantic Scholar team is investigating the application of artificial intelligence in natural language processing, machine learning, human-computer interaction, software testing, and information retrieval.

Semantic Scholar summarises scientific literature in one statement. One of its goals was to overcome the difficulty of reading multiple titles and long abstracts on mobile devices. It also aims to guarantee that the three million scientific articles published each year reach readers. Basically, it is the collection of databases of all the types of research libraries available here; basically, it is domain-independent. We can search the article on this platform by applying some filters such as subject, domain, title, keywords, etc. We provide a link to Semantic Scholar for research aspirants to easily search any research article in any domain. https://www.semanticscholar.org/

8. ScienceDirect

“Science Direct” is a website that gives access to Elsevier’s enormous bibliographic collection of scientific and medical publications. It emphasizes developing a strategy for testing and validation and show how to design a testing and validation program that deliver information in a timely and cost effective manner.

Researchers, teachers, students, healthcare and information professionals use ScienceDirect to improve the way they search, discover, read, understand and share scholarly research.

ScienceDirect combines authoritative, full-text scientific, technical and health publications with smart, intuitive functionality so that users can stay informed in their fields and can work more effectively and efficiently. Article abstracts are freely available, but full text access usually needs a membership or pay-per-view payment, unless the material is freely available via open access.

The following journals are relevant to the software testing domain:

1. Advances in Computers
2. Perspectives on Data Science for Software Engineering
3. Journal of Systems and Software
4. Applied Soft Computing
5. Information and Software Technology
6. Information Sciences.

These journals are very useful for researchers in terms of publication and reference purposes; however, some articles in these journals are author-restricted and require access permission. We’ve included a link to ScienceDirect home page for your convenience https://www.sciencedirect.com/.

Get ready to quick access to check the remarkable software testing platforms

The software development industry has devolved into a front line. Every company wants its product to be the greatest and in order to make your product the greatest on the market; you must ensure that it is of the highest quality.

Conclusion

The primary goal of this blog is to provide a research sources used for references and to help aspiring researchers in their search for researchers.

The post Popular Websites to Find Software Testing Research Papers Free & Paid appeared first on Testbytes.

Introduction to Current Trends

The current trend in software testing that better meets industry demands is the growth of dependability models. Since verification and validation are carried out by distinct people in a typical testing procedure, efficient automation offers a stronger link between the two than does that approach. These automated processes are necessary for real-time applications since they form the core of unified testing, which also includes manual testing.

The technique of manual testing is used to examine the system design and find problems based on test cases. It takes a testing developer with the necessary understanding to complete this repetitious procedure. An automated testing environment is created with multiple levels of abstraction under various domains and a wide variety of diversity rules in order to eliminate the problems with manual testing. Industry-wide automated testing’s overarching goal is to speed up computations and cut costs.

Concept required to grow the Business

Many businesses still use outdated test automation techniques that have negative effects, such as the inability to automate useful tests, produce and maintain automated tests at the lowest possible cost, or promptly identify errors. Such unfavourable consequences prevent businesses from gaining the benefits of test automation they anticipate, use up resources for software development, and potentially jeopardise software quality. “Increasing immature test automation processes with poor effects” is referred to as “improving test automation maturity” in the software industry and research community.

Although there have been more initiatives in the software industry to promote test automation maturity, not all of these efforts have been successful. According to a recent poll on software testing practises, around 65% of the almost 2000 software businesses worldwide sought to increase test automation maturity, but only about half said their efforts were successful. To develop test automation maturity effectively, the industry requires the guidelines.

Software testing, which is based on standard verification over static and dynamic environments, aids in improving product design. In a static environment, verification is carried out using formal processes and methods, but in a dynamic environment, verification is carried out at any level using test cases. Dynamic analysis is more effective than static analysis and can spot mistakes in product design.

Early-stage testing decreases product problems by discovering defects and reducing product failure after implementation. Software testing makes use of straightforward tests and code procedures to cut down on mistakes at every level of the programme. When it comes to the verification and validation processes, the selection of the best product is examined in the former, whilst the product’s qualities are examined in the latter. It enables the client or user to examine the programme that has been created and make necessary corrections to meet their demands.

Read Also: How AI will Change the Software Testing

Data on the item or service being evaluated is gathered during the software testing process. Running programmes or apps while testing allows you to find and repair bugs. The process of developing test cases is crucial for getting the most out of the system and spotting as many errors as possible early. Throughout the whole software development process, this is the most economical use of time and resources. Automated testing is unable to carry out comprehensive testing.

Recent work on a novel application using the Intelligent Security and Automation System (ISAS) architecture has revealed that robotic testing dramatically cuts the price and time required for trials, which is crucial for monitoring a project’s development. It is critical to adhere to the evaluation approach in order to construct the best test suites.

Aspects and Objective

The most important stage of any product development is testing. The testing stage serves as the last checkpoint for any rejections or commission errors. Testing computer programmes is a little more challenging than practising a framework to make sure it functions as intended. When in doubt, it is critical to keep in mind that each inquiry, audit, survey, or walkabout is a test. If there have been more successful static testing attempts, there will be less problems with dynamic testing. IT has frequently demonstrated that the incremental cost of resolving an issue decreases with the speed with which it is identified and fixed. The process of testing starts with the representation of an item. All evolutionary algorithms yield results that are close to those of the Genetic Algorithm. When exposed to a certain set of environmental circumstances, tests are done to make sure that a product or service satisfies specified requirements.

There are two aspects to this objective. The main objective of this fragment is to confirm the accuracy of the requirements point of interest for the item. The settings and code are excellent fits for every criterion, as shown in the second part. For a job to be deemed exact, all affirmation requirements must be satisfied.

Read Also: What is Cloud Testing? Why is Cloud Testing Important

Problems in software test automation

The following actions are mainly responsible for problems in software test automation:

Unfounded hopes for automated testing

It goes without saying that test automation has several advantages. Most significantly, it saves time, energy, and resources for the QA team. Does this not imply that increasing the number of procedures that are automated would increase efficiency? Not exactly. People’s unrealistic test automation expectations can ruin the entire testing process.

Manual testing is important. Manual testing should never be disregarded. In certain cases, manual testing of an application is far superior to developing automated test scripts.

Manual testing is appropriate for:

• Usability testing for UI
• Quick compatibility testing using, say, one or two devices
• One-off tests
• Ad-hoc analysis
• Testing for both localization (L10N) and internationalisation (I18N).

There are still manual testing components. Those who support automated test scripts are necessary. Therefore, even if you insist on automating as much of your testing as possible, there will still be manual tasks to complete.

• Solution: A well-defined testing plan.

Using inappropriate tools

If the proper tools are not used, test automation will not be efficient. With so many testing options available, it is simple to become misled. The poor tool selection might lead to scalability issues in addition to the failure to achieve your initial test automation goals.

• Solution: Make judicious tool selections.

Automate pointless tests while ignoring crucial test cases

Unfortunately, QA teams frequently begin the automation process by arbitrarily automating test cases. They now have an excessive number of pointless tests and little test coverage. Additionally, by using this method, you run the danger of not include all pertinent cases and having poor software quality.

• Solution: Determine what is worth automating.

Incorrect testing window

Many teams still consider testing to be something that happens after development. QAs begin their work only when the entire build has been completed. This antiquated method cannot ensure excellent software quality. It does not allow QAs to adequately test all levels, and there is typically not much time left for testing.

• Solution: Run automated tests concurrently with development.

Inadequate testing

This is the most difficult of the test automation difficulties. A lack of appropriate testing arises when QAs depend too heavily on automation and focus on passing tests rather than detecting issues. The problem is that automation creates a false sense of security. It is tempting to limit human interaction and accountability for the outcomes when automated tests run regularly. Such an approach has disastrous consequences, such as poor test design and inconsistent and inefficient testing.

• Solution: Training for the QA staff.

Improving test automation using genetic algorithm

Software should be tested once it has been created. A study by NIST indicates that software flaws have a significant negative impact. As a result, the test stage of software development is one of the most delicate stages and accounts for around 50% of the total cost. The cost of software testing has been greatly reduced by the introduction of automated software testing tools since it uses a lot of resources but does not offer any new functionality to the product. Various techniques for automatic software testing have been developed over the past ten years with the goal of maximising error detection while generating the fewest amount of test input data possible.

One of the algorithms that has been extensively employed in the area of test data creation automation is the genetic algorithm. Traditional genetic algorithms are effective at producing useful test sets, but they take a long time. Applying prior knowledge to the issue or including a local search phase in the evolutionary cycle can help to some extent. Chromosomes do not attempt to enhance themselves; instead, they only wait for a mutation or recombination to haphazardly improve them. This is a notable flaw in the genetic algorithm. Additionally, the genetic algorithm treats each segment of a chromosome as a whole and does not distinguish between its many sub-sections. Due of this, a genetic search is extremely comparable to a blind search.

The advantages of genetic algorithms are as follows:

• They exhibit an unpredictable pattern that can be statistically assessed but cannot be precisely predicted (i.e., stochastic).
• Another area where it thrives is big data. It works well with probabilistic and non-deterministic rules.
• It’s an excellent tool for simultaneously optimising several objectives. This method has certain shortcomings.
• It can take a lot of time, use a lot of computer resources, and be difficult to create objective functions.

Improving test automation using AI

Automation of the testing procedure and quality control results in considerable time and resource savings. However, there are a few things to think about when using AI and machine learning for test automation. We go through the six factors to take into account when using artificial intelligence and machine learning for test automation.

1. Visual evaluation testing (UI)

Software engineers do visual testing as part of their quality control procedures. They assess if the application functions and appears as intended for the user. Understanding the sorts of patterns that machine learning can detect is crucial. Manual inspectors are better able to spot imperfections, whether they are noticeable, aesthetic, or functional.

While analysing complicated surface textures and picture quality, a typical machine vision system may need a thorough review. Therefore, visual assessment of online or mobile apps is better suited for a deep learning tool or system.

Read Also: A Study Towards Regression Testing Techniques and Tools

It delivers quick and precise outcomes. Developers can rely on this technology in situations when human intervention might be deemed dangerous. Developers may eliminate manual testing and immediately find visual issues by implementing a quick machine learning test.

2. API Testing

Software testing of the Application Programming Interface (API) allows data flow and communication between two software systems. The benefit of API testing is that it is more accurate than UI testing at identifying application flaws. When the test fails, it is simpler to examine the source code. It can withstand application modification, which facilitates automation.

To get thorough test coverage while testing at the API level, you require a greater level of technical know-how and equipment. Additionally, software testers need to be knowledgeable in their respective fields. It is crucial to take into account if testers have a thorough understanding of various application interfaces.

AI allows you to transform manual UI testing into automated API tests that handle all the labor-intensive tasks. You will be able to connect the actions taken in UI tests to API tests as a novice.

3. Domain expertise

In software testing, domain knowledge is essential. With artificial intelligence, you can test apps more effectively whether they are subjected to human or automated testing. For instance, developing test scripts in Java, Python, or C# might be difficult. Advanced testing technologies allow the creation of test scripts and codes by testers. AI enables robots to self-write faultless code.

Manual testing is preferable to handle complicated test scenarios, though. You would be able to determine when to execute test cases using manual or automated testing if you had sufficient domain expertise. Knowing how the application will function and help the company is crucial when implementing AI in test automation. We should typically anticipate failure in results while executing test automation. No matter how little, important, or serious the application fault is, it must be easy for QA teams to scale it.

4. AI Spidering

Writing test scripts for test automation is most frequently done via spidering. It contains a feature that allows you to use AI/ML technologies to direct users to your web application. The application then automatically starts to crawl over itself while scanning and gathering data.

As you perform tests, the tools gradually assemble a dataset and develop patterns for your application. When you use this tool again, it will identify possible problems by referring to its collection of patterns and behaviour.

Though some of the distinctions might not be relevant, keep that in mind. In this instance, a subject matter expert will need to confirm whether the problem that ML has identified is a bug or not.

Understanding which components of an application should be evaluated will be made easier by spidering AI. Simply said, machine learning will handle difficult jobs, and a tester will need to confirm the accuracy of the output.

5. Test Scripts

When a code has been changed, it will be challenging for software testers to estimate how many tests are necessary. Artificial intelligence-based automated testing solutions can determine if a given application needs numerous tests or not.

The use of AI in testing has two advantages. You can save extra time by ceasing to run tests that are not necessary. It is practical to evaluate a system’s overall performance without running the test scripts again. As a result, you don’t need to manually check on it every time.

6. Automated Tests by Robots (RPA)

RPA is the name given to software that automates repetitive commercial tasks without involving any human beings. It assists in fully maintaining and automating the interfaces already present in IT systems. RPA scans the screen, uses the systems to traverse, then locates and collects data.

The tests may be run via online, desktop, or mobile apps, and the duties are entirely handled by the bots. It assists with test data setup and regression test execution.

RPA testing initiatives are undoubtedly being taken by many businesses. However, business testing is powered by RPA, which may reduce the amount of testing that testers complete. Scalability, codeless testing, cost savings, higher productivity, precise findings, and adaptability are among RPA’s key benefits.

Artificial intelligence can automate about half of the test procedures. Machine learning may be used by testers to train systems to find mistakes that manual testing might miss. You may speed up the process of getting reliable findings by incorporating AI into your test automation. Since AI handles the majority of the testing procedure, you can save time.

Automation has a significant impact on the software industry’s ability to increase test efficiency. Projects use extra people for manual testing or utilise automation tools or techniques to increase the amount of test automation in order to ensure test coverage satisfaction and hence lower risk. The option relies on whether the project cycle time or test time is reduced. The significance and necessity of automating software testing have grown as a result of rising software usage, increasingly complicated software functionalities, and shorter timeframes for evaluating programme quality. By applying machine learning to automate software testing, mistakes in manual testing are reduced and assessment is sped up.

Because it helps software testing companies to boost their test efficiency, automation is essential in the software business. In order to assess created software with a range of drawbacks, a number of automated approaches have been conceived to provide test data, approaches based on genetic algorithms being one of them.

The post Improving Software Test Automation Using Different Algorithms appeared first on Testbytes.

A software tester will take part in quality assurance as well as deployment of the software. You will also be in charge of running both automated and human tests to ensure that the code written by developers meet the requirements of the task and that any bugs or errors are fixed before the final product hits the market.

Read Also: What is the Optimum Software Developer to Software Tester Ratio?

The role of a software tester is of great importance to the development of technology-based products, including software systems and cars, electronics, defence as well as healthcare. You could work on specialised, one-of-a-kind projects or globally distributed, multibillion-dollar enterprises.

Software tester as Scrum Master

After doing some in-depth research, I’ve come to the conclusion that some of the qualities needed to be a Scrum Master are already present in the position of a software tester.

• There are more similarities between the two jobs than differences, like:

1. They are in charge of overseeing the procedure and are committed to enhancing quality by assisting the development team in producing high-quality items.

2. They assist in establishing the user acceptability criteria and are knowledgeable about corporate needs.

3. They are confident that every need for admission has been satisfied.

4. Regarding the notion of done, the tester and the Scrum Master ensure that all the user stories are finished at the conclusion of each sprint.

5. They support the software development lifecycle’s overall goal of continual improvement.

6. They promote the development team’s productivity and cooperation.

7. They must plan ahead proactively to reduce pressure during the testing phase and later stages of the software development lifecycle.

8. They can find issues and flaws with the system.

9. They are able to evaluate the danger of any change.

10. They possess social and soft skills

Read Also: What’s The Role of a Computer Game Tester?

• Aside from the traits that these two jobs have in common, each of these roles requires a different set of skills, which help each project progress smoothly.
• As a Scrum Master, the tester develops the ability to observe team activities on a daily basis and get insight into the project’s progress as well as expertise of the product. They also learn how to listen carefully and pay attention to the difficulties the development team faces every day. They proactively stop issues and flaws as a result.
• Having the tester and Scrum Master positions under the same person is advantageous for a team as well as for an organisation operating in Scrum or wishing to change its development process to Scrum. Participating in the development process and gaining knowledge from various stages of the process aid in locating the key difficulties. Due to this, someone who performs the dual roles of tester and scrum master will offer a unique viewpoint, enhancing the entire development process and assisting the team in producing deliverables of higher quality. Additionally, they will benefit the process and be better equipped to influence the development team.
• A tester may also make the ideal Scrum Master, given the correct conditions!

Software tester as Release Manager

• Within the field of software development, the procedures of quality control and release management are intertwined. They are two branches that work closely together and frequently merge to form one team or individual. You could say that QA and RM are inseparable, that they must always be in touch, and that they cannot afford to take a break. In this article, I’ll go over the fundamental rules that every quality tester should adhere to and comprehend regarding the procedure of software release management, bringing both areas together in the pursuit of creating high-quality software.
• A tester’s primary responsibility is to run tests to ensure software quality and verify that earlier work is free of mistakes. The success of the implementation greatly influences how well the testing process goes. The QA team will be able to spot numerous flaws that may be connected to the implementation phase if the tester thoroughly followed the development process and is aware that the back-end and front-end teams ran into specific issues when implementing the code. Consequently, it will also be simpler to suggest the appropriate solution to the appropriate teams.
• Testers will comprehend and value the significance of completing their jobs within the scheduled timeframe if they are aware of how the implementation process has evolved. If testers were properly informed about the software deployment process, many problems may be prevented.
• If the tester and the release manager are the same person, the organization’s working efficiency suffers slightly because they have similar thinking styles and problem-solving perspectives.
• The dependency also decreases, and proficiency increases, which is good for the organization.

Software tester as Project Manager

• The project manager is accountable for the final product’s quality and on-time development. In order to balance the costs of the job, workflow productivity, product dependability, capabilities, and development time, he must use new resources or, if necessary, reallocate those that are already accessible. The issue tracking system’s database becomes a crucial source of knowledge about the present status of the product and its adherence to the requirement specification during this activity.
• An expert in quality assurance (QA) is aware of exactly how software products need to behave instead of how a product manager imagined they ought to. A creative software quality tester shouldn’t have any trouble trying to succeed as a software product manager.
• Who, in essence, is a software tester? A software tester is a remarkable person who serves as the final stage of the web development lifecycle. He stands as the final person between the client and the web developer.

Similar traits of a project manager and a software tester

1. Honesty

• One of the most important talents that software project managers must consistently keep in mind is that it takes their actions, not just their words, to establish a certain business as usual for a group. This skill is comparable to the abilities necessary for software testers. reasonable managerial demands and demonstration of moral behaviour.
• The honesty that underpins project management or leadership refers to a set of principles, a commitment to sincerity, and consistency in teamwork. Sincere software project managers accept responsibility for creating high standards for moral behaviour for themselves and for rewarding those who uphold these standards.

2. They make wise decisions.

• A software tester’s ability to make sound decisions is one of the key duties and obligations of project managers on software projects.
• The excellent project manager position in software project management is crucial for both personal and professional success. The best project management software is used to decide on a number of options that will assist define the project’s course.
• We all know that even a small mistake in a choice may quickly put the entire enterprise in jeopardy. A software tester should be capable of thinking quickly and responding decisively in this way.

3. They Encourage a Common Vision

• In software development, the productive tasks of the project manager are typically characterised as having a vision of the future and the ability to articulate it. A software project manager is a person who empowers you, gives you a sense of purpose, and provides you with the vision and spirit to transform.
• People are empowered by the creative project managers to feel like they truly matter to the work.
• The software tester also gives their co-workers the opportunity to experience their own vision and gives others the option to create their own vision, explore what the vision will mean for their jobs and personal life, or even see their future as a key component of the association’s vision.

4. They are excellent at solving issues!

• The finest project management position in software development is to collaborate with a team of professionals and use their expertise to solve problems in the most effective manner.
• Only the roles and responsibilities of software testers will foresee that the software project managers will have a prepared response in due time regarding every issue; software project managers are required to be able to use the knowledge of their partners and even colleagues to create an aggregate reaction to any issues they encounter on their approach to delivering a project.
• The only person who truly understands how and when it will be best for the end user to interact with the programme is a software tester. This isn’t, exactly, the question of online usability. The only person who can judge if something is done effectively or not is the tester, who uses the software product themselves several times while testing it. A tester then specifies how it must be.

Read Also: 50 Automation Testing Questions for Interview Preparation

Conclusion:-

This article shows how software testers are versatile and able to play various roles along with performing testing tasks. In this article, we will attempt to cover the actual role that the tester can play and how to be helpful in making things easier. After conducting research and consulting with various sources, we concluded that the qualities required of a scrum master are already present in a QA tester.

The software tester is successfully able to do the responsibilities of the release manager; if one person plays both roles, it will help them complete their jobs within the scheduled timeframe. If a software tester takes on the project manager role, the organisation benefits because they share qualities such as honesty, decision-making ability, vision, and problem-solving skills. As per research and literature, it is proved that testers play various roles and are helpful for achieving milestones in the software profession.

The post How Many Roles Can a Software Tester Play appeared first on Testbytes.

Agile development approaches are becoming more popular among companies that are under pressure to provide apps of a better calibre in order to remain competitive. Agile and other iterative techniques are actually taking over as the industry norm for creating applications. Agile’s ideal goal is to accelerate the delivery of the greatest amount of business value possible by putting an emphasis on people and ongoing development. Although the agile technique is typically thought of as primarily relevant to development teams, the entire organisation must adapt.

Agile development confronts businesses with two significant challenges: being flexible enough to keep up with the iterative nature of the agile approach, and providing quality and stability to applications much earlier in the development process in order to align with the business.

The fundamentals of agile testing

The fundamental tenets of agile testing are as follows:

1. Working software is the main gauge of success in this Agile testing strategy.
2. Self-organizing teams have the highest chance of success.
3. Our first aim is to consistently and promptly deliver high-quality software.
4. Daily activity gathering is required of software engineers throughout the project.
5. Increasing agility through steady technology advancement and superior design.
6. Agile testing, which offers continuous input, makes ensuring that the final product lives up to the business’s expectations.
7. The Agile Test approach requires us to carry out the testing process as we implement it, which cuts down on the amount of time needed for development.
8. The Agile testing methodology should focus on maintaining a constant development speed.
9. Regularly reflect on ways to improve your effectiveness.
10. Self-organizing teams provide the finest architectures, requirements, and designs.
11. The team evaluates and modifies its behaviour to improve efficiency at each meeting.
12. The most effective and efficient way to share knowledge within the development team is through face-to-face conversations.

Read Also: Agile Software Development Methodologies

Process of Testing Software

Software testing is a technique for confirming and validating the software; it ensures that the software/applications are executed without errors or problems. An agile model created to satisfy all technical and commercial requirements. When applied, this model may be constructed with the same qualities and will function as intended. Software testing finds program/software bugs, mistakes, and faults. The software testing procedure must include fixing these faults, mistakes, and defects. When programme updates are made, the software should be tested once again and then once more after that, until all flaws have been discovered and corrected. The testing process and the condition of the software under test are monitored and reported on during test operations.

Important flaws are checked during test planning by going over the requirements and design papers. The testing team fixes these flaws but is unable to raise the software’s quality. Prior to testing, all enhancements should be implemented into the system, therefore they should all be recorded during the coding phase of software development. If software architects and designers acquire all the improvements within a certain time limit, they will have created a good model. The design of the software or application can be improved by testing before coding.

Read Also: What is Agile Testing? Process, Methodology and Strategies

Pros and Cons of the Agile Model

Agile methodologies are now extensively used in the software industry, however they might not necessarily be appropriate for all products. The agile paradigm has the following benefits and drawbacks.

The following shows the benefits and drawbacks of the agile model:

Pros

• Is an extremely practical method for developing software.
• Encourages collaboration and cross-training.
• Functionality can be quickly built and proven.
• Minimum resource requirements.
• Adaptable to both changing and fixed needs
• Provides early, imperfect answers.
• Effective model for continuously changing surroundings.
• Few rules, simple to use documentation.
• Allows development and delivery to occur concurrently within a larger, planned environment.
• Requires little to no planning
• Simple to handle
• Provides developers with flexibility
  
  Cons
  
• Ineffective for managing complicated dependencies.
• A greater risk of extensibility, maintenance, and sustainability
• Without an overarching strategy, an agile leader, and an agile PM practise, it will not succeed.
• The scope, functionality to be supplied, and modifications to fulfil deadlines are determined by strict delivery management.
• Relies significantly on client contact; as a result, if the consumer is unclear, the team may be led astray.
• Since little documentation is produced, there is a great deal of individual dependence.
• The absence of documentation may make it difficult for new team members to learn technology.

Commercial Agile Testing Methodology

Agile testing is currently widely utilised in industries since it entails close customer participation and short week cycles. Due to all these qualities, the project moves very quickly. The shortcomings of the V-Model and the Waterfall Model are eliminated, making it the optimum technique.

For projects with shifting needs and unclear project scope, it is the optimum model. Customers are more confident and satisfied with the finished product as a result of the regular customer participation at every stage, which also reduces the likelihood of future defects. Since there is client engagement throughout every cycle, the final product that is given at the conclusion of each cycle meets the criteria.

Agile testing also lowers project costs since workable products are supplied in increments after each cycle, reducing the likelihood of future defects. Additionally, this process improves communication and team trust in QA.

Due to its benefits, lower delivery costs, and other qualities in the modern industry, Agile is now a new and one of the methodologies that takes the longest to adopt.

Read Also: Agile VS DevOps: Difference between Agile and DevOps

Distinguish between Agile Testing and Waterfall Testing

The Development Life Cycle activities take place in phases that are sequential in a Waterfall Development approach. As a result, testing is a distinct phase that begins only after the development phase is over.

The key distinctions between Agile Testing and Waterfall Testing are as follows:

Conclusions

The agile method has been in use for a while. It has proven essential in many of the intricate projects that both small and large businesses are now working on. The most creative businesses of today and future will keep pushing the boundaries of agile methods. For them, the ability to develop, plan, and carry out initiatives successfully in a fast-paced, dynamic environment will be the difference between just existing and thriving. Making the proper judgments throughout project execution as well as planning is a key component of agility.

Testing methods, skills, techniques, and equipment may need to shift in order to handle that sort of change. The mechanics of test execution are one area of software testing that does not change merely because the project team is utilising an agile strategy to build software, although certain testers may need to significantly alter their testing methodology if they are to be useful on an agile software project. Agile testers must decide what work to complete next, how to complete it, how to make it relevant to the client, and how to exercise the application in various ways to enhance their understanding of how things operate and potential risk areas.

The post An Analysis of the Effects of the Agile Model in Software Testing appeared first on Testbytes.

When should you do regression tests?

1. When a new feature is added to the system and the code is changed to accommodate and incorporate that feature with the current code.
2. When a software flaw has been found and is being fixed by debugging the code.
3. When a code change is made to improve performance.

Regression testing procedure:

Initially, anytime we make changes to the source code for whatever reason—such as adding new functionality or optimising existing code—our software fails in the previously created test suite for apparent reasons when it is run. After the failure, the source code is debugged to find the program’s faults. The necessary changes are done after finding the problems in the source code. Then suitable test cases are chosen from the test suite that already exists and covers all the updated and impacted portions of the source code. If more test cases are needed, we can add them. Regression testing is ultimately carried out utilising the chosen test cases.

• All test cases are chosen with in this manner from the test suite that is already in place. Although it is the simplest and safest method, it is not very effective.
• Randomly choose test cases: This strategy involves choosing test cases at random from the test suite already in place; however, it is only effective when every test case has an equal capacity to identify faults, which is extremely uncommon. Because of this, it is rarely used.
• Choose test cases that cover and test the updated portions of the source code and the parts that are affected by these modifications are chosen in this approach.
• Pick higher priority test cases: In this method, each test case in the test suite is given a priority code based on its capacity to identify bugs, client requirements, etc. The test cases with the greatest priorities are chosen for regression testing after giving the priority codes.The test case with the highest priority is ranked first. A test case with priority code 2 is less significant than one with priority code 1, for instance.

Read Also: Difference Between Regression Testing and Retesting

• Tools for regression testing: In regression testing, we often choose test cases from the current test suite itself, ; so, we don’t need to compute their expected outcome and it can thus be readily automated. Automating the regression testing process will be extremely effective and time saving.

The following are the most often used regression testing tools:

• Selenium
• WATIR (Web Application Testing In Ruby)
• QTP (Quick Test Professional)
• RFT (Rational Functional Tester)
• Winrunner

Regression testing provides the following benefits:

• It makes sure that no new defects have been created after the system has received new functionality.
• Since the majority of the test cases chosen for regression testing are already part of the test suite, their anticipated results are known. As a result, automated tools may readily automate it.
• It aids in preserving the source code’s quality.

The drawbacks of regression testing include:

• If automated tools are not employed, it may take a lot of time and resources.
• Even after relatively minor modifications to the code, it is necessary.

Selenium

It is one of the best automated tools for testing web applications for regression. You may use Selenium Web Driver to create robust browser-based regression automation suites and tests.

A selection of Selenium’s functionalities is available for automating web applications. It is still one of the best tools available for cross platform and browser-based regression testing. Data-driven testing and automated test scripts that cycle over data sets are supported by Selenium. For large-scale quality assurance teams with knowledgeable testers, this is the right course of action. However, small and mid-size teams struggle with its high learning curve.

Features of the Tool:

1. Selenium supports several OSs, browsers, and environments.
   It works with many different programming languages and testing frameworks.
2. It is, without a doubt, a fantastic tool for performing regular regression testing.

Read Also: Optimum Software Developer to Software Tester Ratio?

WATIR (Web Application Testing In Ruby)

A Ruby programming language-based open-source package, Watir stands for Web Application Testing in Ruby. It allows for the creation ofto create and maintain simple-to-read and maintain tests on a light and adaptable user interface.

Watir enables a range of user interaction features for website testing, including the ability to click links, complete forms and validate texts across a number of browsers.

Features of the Tool:

• Extremely portable and user-friendly gadget
• Excellent browser interaction features are included with this programme.
• Designed to test web applications.
  Enables the creation of understandable, maintainable, and easy automated tests.
• Support for cross-platform technologies
  Numerous large corporations, like SAP, Oracle, Facebook, etc., use it
• Technology independent

QTP (Quick Test Professional)

It is an automated functional testing tool that consumes high resources and requires a license. QTP provides customer support in the form of selenium community forums. For parameterization in QTP, built-in tools are available. QTP supports only Windows and VB Script. There is support for testing on both web and desktop-based applications. There is built-in test report generation within the tool QTP, and there is a built-in object repository in QTP. QTP is user-friendly, and there is a build-in recovery scenario in QTP. Browsers supported in QTP are specific versions of Google Chrome, Mozilla Firefox, and Internet Explorer.

Benefits of Automation using QTP

• It allows for recording and replay.
• It allows testers refer to the screen object attributes when recording scripts on an active screen.
• It has a great system or procedure for identifying objects.
• It supports a variety of add-ins, including those from PeopleSoft, Oracle, Java, SAP, NET, and others.
• Through an active screen, you may improve the current tests even without the AUT.
• It supports well-known automation frameworks, including data-driven testing, modular testing, and keyword testing.
  It has an internal IDE.
• It can be connected with test management programmes like Winrunner, Quality Center, and Test Director.
• It is simple to manage several suite kinds, including Smoke, Regression, and Sanity.
• It works with XML.
• Through QTP, test reporting is possible for analytical purposes.
  Easily maintained.

RFT (Rational Functional Tester)

An object-oriented automated functional testing tool called Rational Functional Tester can run automated functional, regression, GUI, and data-driven tests. HTML, Java,.NET, Windows, Eclipse, SAP, Siebel, Flex, Silverlight, Visual Basic, Dojo, GET, and PowerBuilder programmes are just a few of the many applications and protocols that RFT supports.

Benefits

The following are the primary advantages of Rational Functional Tester:

• Reusability: Tests may be immediately performed on several iterations of an application, cutting down on the time required for regression testing.
• Consistency: The exact same actions will be taken each time an RFT script-based test is executed.
• Productivity: Automated testing is quick and flexible, requiring no additional resources.
• Rational Team Concert and Rational Clear Case are two source control management technologies that RFT interfaces with. Users may manage their RFT functional test assets using either Rational Clear Case or Rational Team Concert by integrating RFT with these source control management programs.
• RFT and Rational Quality Manager have excellent integration. Users may run test scripts from within Rational Quality Manager after integrating RFT with RQM using the adaptor.

Features

• Broad skills match: The RFT tool is designed for users with a range of technical skills to ensure that your quality assurance team is not limited to basic testing and that other subject matter experts in your company can participate in and comprehend the test flow using a visual storyboard format.
• New software versions can employ user interface features that technology has learned, reducing time spent writing new test scripts.
• Automated scripts – Rational Functional Tester gives your development teams the ability to write scripts with keyword associations that are simple to reuse, increasing productivity.
• Using the Eclipse Java Developer Toolkit editor, your team may quickly and easily develop Java test scripts. It includes sophisticated debugging features and automates code completion

Read Also: How Much Does App Testing Cost?

Winrunner

For functional testing, Win Runner is a popular automated software testing tool. Mercury Interactive created it. C and web technologies including VB, VC++, D2K, Java, HTML, Power Builder, Delphe, and Cibell (ERP) are supported. WinRunner makes it simple to create tests by capturing your application development process. You may navigate your application’s GUI (Graphical User Interface) elements by pointing and clicking.

A test script written in the C-like Test Script Language is generated by WinRunner. With hand programming, we can improve our test scripts even further. The Function Generator, which is part of WinRunner, makes it simple and quick to add functions to our recorded tests.

The crucial features of WinRunner include:

• We are able to do functional and regression testing on a range of application software created in languages, including PowerBuilder, Visual Basic, C/C++, and Java. Additionally, we are able to test ERP/CRM software programs.
• Runs tests on many browser settings, including Internet Explorer and Netscape Navigator, as well as all versions of the Windows operating system.
• In the “record” mode, we may record GUI operations. A test script is automatically generated by WinRunner.
  To compare actual and anticipated outcomes, we can add checkpoints. Bitmap checkpoints, GUI checkpoints, and web links are some examples of the checkpoints.
• It offers a tool for test case synchronization.
  A recorded test may be transformed into a data-driven test using Data Driver Wizard. So, within a test script, we may swap out data for variables.
• During automated testing, database checkpoints are utilised to validate data in a database. We will maintain database integrity and transaction correctness by highlighting the records that are added, removed, changed, or updated.
• WinRunner is taught to detect, record, and replay custom objects using the Virtual Object Wizard.
• The reporting tools offer the ability to create test results automatically and examine flaws.
• Many testing-related tasks may be automated by integrating WinRunner with the testing management programme, TestDirector.

Silktest

Silk Test is a solution for corporate application regression and function testing. It was first created by Segue Software, which Borland purchased in 2006. Micro Focus International purchased Borland in 2009. QA Partner was the original name of the product from 1993 to 1996.

Silk Test provides a range of clients:

• Silk Test Workbench supports visual automated testing (much like the previous TestPartner) and uses the VB.Net scripting language.
• For automated scripting, Silk Test Classic makes use of the domain-specific 4Test language. Similar to C++, it is an object-oriented language. Classes, objects, and inheritance are all used.
• UFT Developer, formerly known as Silk4J, enables automation in Eclipse using Java as the scripting language.
• UFT Developer, formerly Silk4Net, enables the same functionality in Visual Studio using VB or C#.

Conclusion

An essential component of software development processes is test automation. Similar to this, automated regression testing is regarded as a crucial component.

Product teams may obtain more detailed feedback and react immediately with a speedy regression testing procedure. Regression testing finds new vulnerabilities early in the deployment cycle, saving firms the expense and work of fixing the accumulated flaws. Apparently, a little change can occasionally have a cascading effect on the product’s essential capabilities.

Because of this, developers and testers must not let any modification—no matter how small—that falls outside of their sphere of influence.
Functional tests don’t take into account how new features and capabilities interact with the old ones; they merely look at how they behave. Therefore, determining the main cause and the product’s architecture is more challenging and time-consuming without regression testing.

The post A Study Towards Regression Testing Techniques and Tools appeared first on Testbytes.

Testing was viewed until the late 1970s as a process to make sure the programme met the requirements. After then, in addition to making sure the software was running properly, it was expanded to detect the faults. The testing process was also thought of as a way to gauge quality in the 1980s. As a result, it was given more significance and was handled as a process that was part of the software development life cycle and was explicitly defined and monitored. The testing procedure established its own life cycle by the middle of the 1990s.

Read Also: Difference Between Software Tester VS Developer

Better understanding of software cost estimate is required to improve the realism of software development project bids and budgets. Instead of generic software development, we wanted to see empirically supported methods for estimating software effort costs, but the research turned up only standard COCOMO models, function points, expert judgments, and a few formal models that had already been established, indicating their maturity in both academia and industry.

The research is devoted to a single, unified vision for empirically based software effort cost estimation in testing, which is not addressed by the articles, publications, research, and studies. The documents a huge set of publications, innovations, and advancements in evidence-based assessment of software cost effort in verification, validation, and testing are systematically analysed through study reviews, which are crucial for their systematic analysis. Evidence-based software engineering (EBSE) is a branch of science that collects data from real-world industrial settings in order to determine the likelihood of study outcomes. Despite not always reflecting the actual practise environment, random controlled experiments are also evidence-based.

Read Also: Top 10 Websites to Learn Software Testing in 2023

The Evolution of Software Testing

In early days of software development, software testing was considered only a debugging process for removing errors after the development of software.

We can divide the evolution of software testing into the following phases;

• Debugging oriented phase
• Demonstration oriented phase
• Destruction oriented phase
• Evaluation oriented phase
• Prevention oriented phase
• Process oriented phase

Debugging oriented phase:- 

This stage represents the initial testing phase. The fundamentals weren’t recognised back then. Programmers wrote programmes and then tested them until they were certain that all flaws were fixed. Checkout, a word for testing that concentrated on getting the system to function, was used.

Demonstration oriented phase:-

In this stage, debugging kept going. It is understood in 1957 that the goal of checkout is not only to execute the software but also to show that it complies with the stated criteria. As a result, the range of the programme check-up expanded from programme runs to programme accuracy.

Destruction oriented phase:-

Here, the definition of testing was altered to “testing is to detect more and more errors” rather than “testing is to prove the absence of errors.” In this stage, the value of early testing was also recognised.

Evaluation oriented phase:-

In this stage, emphasis is placed on software product quality so that it can be assessed at every level of development.
When compared to issues discovered during the implementation or post-implementation phases, it was less expensive to troubleshoot problems that were discovered early in the development process.

Prevention oriented phase:-

The evaluation model stressed on concept of bug prevention as compared to earlier concept of bug-detection.
With the idea of early detection of bugs in earlier phases, we can prevent the bugs in implementation.

Process oriented phase:-

In this stage of the software development life cycle, testing was developed as a full procedure rather than a single stage (executed after coding)
The testing procedure begins as soon as a project’s requirements are established and proceeds concurrently with the SDLC (Software Development Life Cycle)

Software testing 1.0:-

In the SDLC, software testing was only seen as a single phase that came after coding. There was no test organisation. There were a few testing tools available, but their usage was restricted by their high price.
There was no high standard.

Software testing 2.0:-

Software testing started to take centre stage in this phase of the SDLC, and early testing became a thing. Numerous testing tools were also available at this time since testing was moving toward resource planning.

Software testing 3.0:-

Software testing is now evolving into a procedure that is focused on strategic effort. It implies that there should be a procedure that provides us with a general roadmap for the testing process. Goals for quality should be the driving force. In this phase, management is actively involved.

 Software Testing Epoch:-

During this time, development and testing were viewed as mutually independent tasks. The testing team received the programme after it was finished and verified it. During the requirement analysis phase, testers were not very actively involved and only sometimes interacted with business stakeholders. They were primarily reliant on information that was imparted to them through documentation created during design and development or learning from programmers who developed the code.

The testing team’s adoption of restricted testing methodologies was a result of their lack of understanding of the needs and expectations of the clients. Based on their comprehension of the documentation, the testers would create a test plan and conduct ad-hoc testing of the programme. It is clear that there were certain restrictions, and the testing was not exhaustive. Testing developed, and the next phase was the period of exploratory and manual testing.

Manual Testing and Exploration:-

Agile testing, exploratory testing, and other approaches became popular in the late 1990s. Manual testing was carried out with the use of thorough test designs and test cases. By examining the programme within the scope of testing charters, exploratory testing allowed users to test and break software in their own unique ways. The software development process needs more sophisticated testing methods due to its rapid and extensive growth. Agile testing’s gradual and iterative methodology contributed to the accomplishment of this objective. The repetitious tests those were able to be automated thanks to iterative testing.

The Age of Automation:-

Numerous fresh ideas that emerged with the turn of the millennium completely transformed software testing. These methods completely altered how testing was done. The SDLC was now considered to include testing at every stage. Quality control and assurance have become more important at every stage.

Automation raised the bar for testing significantly. The testers were further given the tools they needed to do their duties more effectively thanks to the abundance of automated testing frameworks. Automation made it possible to quickly and accurately carry out sanity and regression tests.

The testing procedure needed to be scaled up throughout this time period as well. The firm was able to manage product testing more quickly and with less infrastructure expenditure thanks to crowdsourcing and cloud testing.

The Continuous Testing

Customers now anticipated seeing an early functioning prototype of the finished product as the business dynamics started to shift. As a result, there was an increase in demand for regular and basic software releases. High connection and faster testing and deployment times across many platforms were made possible by enhanced network infrastructure.

This made delivery more frequent, which unintentionally resulted in additional testing effort. Continuous Integration and Continuous Deployment become well-known concepts. Continuous testing also acquired significance along with these. Shorter delivery cycles resulted from the rise of DevOps and CI/CD. It became essential to carefully evaluate threats in real time. At every level of the software development life cycle, risk assessment and management were required.

The business stakeholders expected intermediate releases under strict deadlines without sacrificing the final product’s quality. Continuous testing required to advance to become more effective in order to keep up with these expectations. This is where utilising artificial intelligence in testing comes into play.

A New Age of Artificial Intelligence:-

Artificial intelligence, put simply, is the ability of a computer to mimic human behavior through perception, comprehension, and learning.

The predictive analysis of data serves as the foundation for AI systems. This also implies that data is crucial for AI testing.
Today, a variety of testing solutions driven by AI are available to assist with unit testing, API testing, UI testing, etc. Visual testing is a prime illustration of how AI is used in testing.

Read Also: Optimum Software Developer to Software Tester Ratio?

What is Test Estimation?

For a specific software testing project in a specific environment utilising certain methodologies, tools, and techniques, test estimation is the estimation of the testing size, testing effort, testing cost, and testing timeline.

1. Estimation, which was previously defined in the topic
2. Testing Size – The quantity (amount) of testing that must be done. Sometimes, especially in embedded testing (where testing is integrated into the software development activity itself), this may not be estimated.
3. Testing Effort – The number of person days or person hours required to carry out the tests
4. Testing Cost – the costs associated with conducting tests, including the cost of human labour
5. Testing Schedule – the number of days or months in a calendar year required to conduct the tests.

Conclusion

Because it depends on the complexity and efforts employed for the specific product, the effort calculator is crucial for cost prediction as well as the ratio of testers to developers. The amount of time the product takes also matters a lot. Nowadays, automated and AI-based testing is a common technique, but as my research shows, all modern techniques are based on manual testing.

Because it depends on the complexity and efforts employed for the specific product, the effort calculator is crucial for cost prediction as well as the ratio of testers to developers. The amount of time the product takes also matters a lot.

To overcome the problem of software test effort estimation Testbytes creates a test effort calculator for cost estimation, which is used to estimate the amount and time frame required for testing your software. The Testbytes software test effort calculator is designed for specification and user preferences.

The test cost calculator has many domains, such as banking and finance, telecom, e-commerce, etc. The cost calculator is platform-independent, i.e., you can select web, mobile, or both platforms at the same time. The total number of testing cycles required for the entire process determines the final cost.

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