Category: Software Testing Help

It is common for companies to introduce minor changes from time to time to their products.

However, introducing these changes affects the application in numerous ways, such as its functionality, performance, bugs, etc.

Therefore, it is important to keep the testing process on whether the software is on the market or a small change has been introduced.

Conducting this type of testing is known as regression testing.

What is Regression Testing?

Regression testing is a type of software testing that aims to ensure that recent code changes have not adversely affected existing features. It involves re-running test cases that have been executed in the past to verify that the existing functionality still performs as expected after the introduction of new code.

The primary goal of regression testing is to uncover any defects that may have been inadvertently introduced as a result of the code modifications. This type of testing helps maintain the overall integrity of the software and prevents the reoccurrence of previously fixed bugs.

Benefits of Regression Testing

While the basic aim behind conducting regression testing is to identify bugs that might have developed due to the changes introduced, conducting this test benefits in a number of ways, such as:

• Increase chances of detecting bugs caused due to new changes introduced in the software
• Helps in identifying undesirable side effects that might have been caused due to a new operating environment
• Ensures better-performing software due to early identification of bugs and errors
• Highly beneficial in situations when continuous changes are introduced in the product
• Helps in maintaining high product quality

Types of Regression Testing

There are a number of ways in which this testing can be done. However, this depends on factors such as the type of changes introduced, bugs fixed, etc.
Some of the common types of regression testing include:

1) Corrective Regression Testing:

Corrective regression testing is a type of software testing that focuses on verifying that specific issues or defects, which were identified and fixed in the software, have been successfully resolved without introducing new problems. The primary goal is to ensure that the changes made to address reported bugs or issues do not negatively impact the existing functionality of the application.

Here’s an example of corrective regression testing:

Scenario: Corrective Regression Testing for Login Functionality

Initial State:

• Application with a login page.
• A bug was reported stating that the application allows access with incorrect credentials.

Bug Details:

• Bug ID: BUG-12345
• Description: Users can log in with invalid credentials.

Steps to Reproduce (Before Fix):

1. Open the application login page.
2. Enter an invalid username.
3. Enter an invalid password.
4. Click on the “Login” button.
5. Verify that the user is logged in, despite providing incorrect credentials.

Steps to Fix:

1. Developers investigate and identify the code causing the issue.
2. Code is modified to validate user credentials properly.
3. The fix is implemented and tested locally.

Corrective Regression Testing:

Positive Test Case (After Fix):

Test Steps:

1. Open the application login page.
2. Enter valid username.
3. Enter valid password.
4. Click on the “Login” button.

Expected Result:

• User should be successfully logged in.
• Verify that the user is redirected to the dashboard.

Negative Test Case (After Fix):

Test Steps:

1. Open the application login page.
2. Enter invalid username.
3. Enter invalid password.
4. Click on the “Login” button.

Expected Result:

• User should not be logged in.
• An error message should be displayed.

@Test
public void testCorrectiveRegression() {
// Positive test case (after fix)
// Test steps to open login page, enter valid credentials, and click login
// Assert statements to verify successful login and redirection

// Negative test case (after fix)
// Test steps to open login page, enter invalid credentials, and click login
// Assert statements to verify login failure and error message presence
}

This example demonstrates how corrective regression testing ensures that the specific bug (allowing login with invalid credentials) has been successfully addressed without introducing new issues in the login functionality.

2) Retest-all Regression Testing:

Retest-All regression testing, also known as a complete regression test, involves re-executing the entire test suite, including both new and existing test cases, to validate the modified code.

In this approach, every test case is retested to ensure that the changes made to the software have not introduced any new defects and that the existing functionalities remain unaffected.

Example: Suppose a software application undergoes a major update, and several changes are made to the codebase. In a retest-all regression testing scenario, the testing team would execute all the test cases, covering various features and functionalities of the application, to verify that the changes have not caused any unintended side effects. This comprehensive approach ensures that the entire application is thoroughly validated, providing confidence in the stability and reliability of the updated software.

Also Read:-  Top 25 Software Testing Companies to Look Out For in 2024

3) Selective Regression Testing:

Selective regression testing is a software testing strategy where a subset of test cases is chosen based on the areas of the code that have undergone changes. The goal is to verify that the recent modifications have not negatively impacted the existing functionality of the application.

Here’s an example of selective regression testing:

Scenario: Selective Regression Testing for E-commerce Checkout Process

Initial State:

• An e-commerce application with a functional checkout process.
• Recent changes were made to optimize the checkout page.

Changes Made:

• Developers modified the code related to the payment processing module to improve performance.

Steps to Perform Selective Regression Testing:

1. Identify the Modified Area:
   • Identify the specific module or area of the application that has undergone changes. In this case, it’s the payment processing module.
2. Select Test Cases:
   • Choose a subset of test cases related to the payment processing and checkout process. Consider scenarios that the recent changes are likely to affect.
3. Execute Test Cases:
   • Execute the selected test cases to ensure that the recent modifications have not introduced defects in the payment processing functionality.
4. Validate Existing Functionality:
   • While the primary focus is on the modified area, it’s essential to validate that existing functionality outside the modified scope continues to work as expected.

Example Test Cases for Selective Regression Testing:

Test Case 1: Positive Payment Processing

Test Steps:

1. Add items to the cart.
2. Proceed to the checkout page.
3. Enter valid shipping details.
4. Enter valid payment information.
5. Complete the purchase.

Expected Result:

• Payment is processed successfully.
• Order confirmation is displayed.

Test Case 2: Negative Payment Processing

Test Steps:

1. Add items to the cart.
2. Proceed to the checkout page.
3. Enter valid shipping details.
4. Enter invalid payment information.
5. Attempt to complete the purchase.

Expected Result:

• Payment failure is handled gracefully.
• User receives an appropriate error message.

Selenium Code (Java):

@Test
public void testPositivePaymentProcessing() {
// Test steps to simulate positive payment processing
// Assert statements to verify successful payment and order confirmation
}

@Test
public void testNegativePaymentProcessing() {
// Test steps to simulate negative payment processing
// Assert statements to verify proper handling of payment failure and error message
}

In this example, selective regression testing focuses on a specific area (payment processing) that underwent recent changes. The chosen test cases help ensure that the optimizations made to the checkout page did not introduce issues in the payment processing functionality.

4) Progressive Regression Testing:

Progressive regression testing is an approach in software testing where new test cases are added to the existing test suite gradually, ensuring that the application’s new features or modifications are thoroughly tested without compromising the testing efficiency. It involves building upon the existing test suite with each development cycle, making it a continuous and evolving process.

Example Scenario: Progressive Regression Testing in an E-learning Platform

Initial State:

• An e-learning platform with features like course enrollment, quiz submissions, and user profiles.
• Ongoing development to introduce a new feature: real-time collaboration on assignments.

Development Cycle 1:

• Developers implement the initial version of the real-time collaboration feature.

Progressive Regression Testing Steps:

1. Existing Test Suite:
   • The current test suite includes test cases for course enrollment, quiz submissions, and user profiles.
2. Identify Impact Area:
   • Identify the potential impact of the new feature on existing functionality. Focus on areas such as user profiles, user interactions, and database changes.
3. Create New Test Cases:
   • Develop new test cases specifically targeting the real-time collaboration feature. These may include scenarios like simultaneous document editing and version control.
4. Add to Test Suite:
   • Integrate the new test cases into the existing test suite.
5. Execute Test Suite:
   • Run the entire test suite, covering both existing and newly added test cases.
6. Review and Update:
   • Review the test results and update the test suite based on any identified issues or changes in the application.

Progressive Regression Testing Cycle:

7. Development Cycle 2:
   • Developers enhance the real-time collaboration feature and introduce another new feature: discussion forums.
8. Repeat Steps 2-6:
   • Identify the impact area, create new test cases for the discussion forums, integrate them into the test suite, and execute the updated suite.

Selenium Code (Java) for Progressive Regression Testing:

@Test
public void testRealTimeCollaboration() {
// Test steps for real-time collaboration feature
// Assertions to validate collaboration functionalities
}

@Test
public void testDiscussionForums() {
// Test steps for discussion forums feature
// Assertions to validate forum interactions
}

5) Complete Regression Testing:

6) Manual Regression Testing

Manual Regression Testing involves re-executing existing test cases without the use of automated tools. It relies on human testers to manually perform the testing steps and verify the results.

Key Characteristics:

• Human-Driven: Testers manually execute test cases, relying on their expertise and judgment.
• Flexibility: Allows for exploration and adaptation of test cases during execution.
• Suitable for Complex Scenarios: Effective for testing intricate user interactions or scenarios that are difficult to automate.
• Time-Consuming: Can be slower than automated testing, especially for large test suites.
• Prone to Human Error: Testers may inadvertently introduce errors during manual execution.

Example:

Consider a web application that has undergone changes to its checkout process. To ensure the changes haven’t introduced regressions, a tester would perform manual regression testing by:

1. Reviewing Test Cases: Analyzing existing test cases covering the checkout process.
2. Executing Test Steps: Manually navigating through the checkout steps, entering data, and clicking buttons as specified in the test cases.
3. Observing Results: Carefully observing the application’s behavior, checking for errors, unexpected outcomes, or inconsistencies.
4. Comparing Results: Verifying that the observed behavior matches the expected behavior defined in the test cases.
5. Reporting Issues: Documenting any bugs or defects found during testing.

7) Unit Regression Testing

Unit Regression Testing involves the testing of individual units or components of a software application to ensure that new code changes or modifications do not adversely affect the existing functionalities. It focuses on verifying the correctness of specific units of code after each change, providing quick feedback to developers. Below is an example scenario demonstrating Unit Regression Testing.

Example Scenario: Unit Regression Testing for a Login Module

Initial State:

• A web application with a login module containing functions for user authentication.
• Ongoing development to enhance the security features of the login process.

Unit Regression Testing Steps:

1. Existing Unit Test for Login Functionality:
   • Initial unit tests cover basic login functionality, checking username-password validation.
2. Development Cycle 1:
   • Developers implement changes to enhance security, introducing two-factor authentication (2FA).
3. Unit Regression Testing Cycle:a. Identify Affected Units:
   • Identify the precise components or operations of the login module that the security enhancement affects.

   b. Modify Existing Test Cases:

   • Update existing unit test cases for the login module to include scenarios related to 2FA.

   c. Create New Test Cases:

   • Develop new unit test cases specifically targeting the new security features, such as testing OTP (One-Time Password) generation and validation.

   d. Execute Unit Tests:

   • Run the modified and new unit tests to verify the correctness of the login module’s updated code.

   e. Review and Update:

   • Review the test results, update unit tests based on any identified issues, and ensure that the existing functionality remains intact.

Unit Regression Testing Code (Java) for Enhanced Login Module:

Quick Note

• It is important to make sure that the type of regression testing that needs to be conducted is selected appropriately.
• This depends on various factors, such as areas of recurrent defects, the criticality of the features, etc.
• But what remains a priority is ensuring that the software delivers the best functionality and proves to be a beneficial addition to the industry.

Importance of Regression Testing

• Regression tests are designed to ensure that the code does not regress while debugging is underway.
• One of the greatest benefits of unit tests is that they are automatically regression tests. After those tests are written, they will be executed subsequently each time you modify or add new features. It is not necessary to write regression tests explicitly.
• A regression test is a test that you run on a regular basis to make sure your fix is still in effect and functioning after you have fixed the bug. It also serves as validation that you have fixed the bug.
• Validates that previously developed and tested software remain reliable after modifications.
• Identifies and prevents the introduction of defects during the software development life cycle.
• Enhances overall software quality by maintaining consistent performance across iterations.
• Provides confidence to stakeholders that the software continues to meet specified requirements.
• Supports the Agile development process by enabling continuous integration and delivery.
• Detects unexpected interactions between different software modules or components.
• Saves time and resources by catching issues early, reducing the cost of fixing defects later in the development cycle.
• Facilitates the smooth evolution of software, allowing for iterative improvements while maintaining stability.

When To Carry Out Regression Testing?

1. Change in Requirements: Whenever there is an alteration in the project’s requirements, and corresponding code modifications are implemented to align with the new specifications.
2. Introduction of New Features: When new features are added to the software, ensuring that the existing functionalities remain intact and unaffected by the addition.
3. Defect Resolution: After addressing and fixing defects or bugs in the software, regression testing ensures that the corrections do not inadvertently impact other parts of the system.
4. Performance Issue Resolution: Whenever performance-related issues are identified and rectified, regression testing validates that the changes made do not compromise the overall performance of the software.

Difference between Regression Testing and Retesting

The fact is that both are entirely different.

Regression testing ensures that any update made to the code does not affect the existing functionality, whereas retesting is carried out when test cases find some defects in the code.

And when those defects are fixed, the tests are done again to check whether the issues are resolved.

Retesting is to ensure whether the defects are resolved, whereas regression testing detects probable defects caused by the changes made to the code.

Challenges of Regression Testing

• Regression testing forms an important phase of STLC but brings along several challenges for the testers.
• It is time-consuming; it requires rerunning a complete set of test cases again for a complete set of code.
• Updates make your code more complex; they even increase the set of test cases for regression testing.
• Regression testing ensures that updates bring no flaws to the existing code.  However, considering the time taken to complete regression testing, it becomes hard to make non-technical clients understand the value of regression testing.

Also Read: Performance Testing -Types, Stages, and Advantages

Tools For Regression Testing

1) Ranorex Studio
2) SahiPro
3) Selenium
4) Watir
5) TestComplete
6) IBM Rational Functional Tester
7) TimeShiftX
8) TestDrive
9) AdventNet QEngine
10) TestingWhiz
11) WebKing
12) Regression tester
13) silktest
14) Serenity
15) QA wizard
To read about them in detail click here!

Frequently Asked Questions / FAQs

1. What is regression testing?

Regression testing is a method of software testing that involves rerunning a set of test cases to guarantee that recent code changes did not negatively affect previously existing functionalities. It focuses on identifying any accidental side effects that may have been introduced during the process of development or maintenance.

2. What are some of the popular regression testing approaches?

The general approaches to the process of regression testing include re-running automated test scripts, manually retesting critical functionalities, using version control systems to compare code changes, employing continuous integration tools for automated builds and tests, and utilizing test automation frameworks that support regression testing.

3. How frequently should regression testing be done?

The development cycle and code change rate are what determine the regression frequency. In agile development, regression testing normally takes place after every iteration, while in waterfall models it happens within the test phase or before release. CI practices also facilitate frequent regression testing by each code commit.

4. What problems are present in regression testing?

The drawbacks of regression testing may involve:

choosing and maintaining an efficient group of test cases, 

• selecting and maintaining an effective set of test cases, 
• managing the testing environment and data, 
• dealing with time constraints,
• balancing the trade-off between thorough testing and quick feedback. 

Automated regression testing may also face challenges related to script maintenance and false positives/negatives.

5. How do you select test cases for regression testing?

Test case prioritization in regression testing requires that critical and more often used functionalities should be given high priority. The criteria for prioritization can be the business impact, risk analysis, or the areas of the application that are most susceptible to change. This guarantees that efforts of testing are aimed at the most critical issues to optimize the process of testing.

Testing is an integral part of the software development life cycle. Various models or approaches are used in the software development process, and each model has its own advantages and disadvantages. Choosing a particular model depends on the project deliverables and the complexity of the project.

What Are Software Testing Models?

Software testing models are systematic approaches used to plan, design, execute, and manage testing activities. They provide guidelines for carrying out testing processes effectively and ensure comprehensive test coverage.

Each model offers distinct advantages and is chosen based on the specific requirements of the project and the organization’s preferences. Understanding these models is crucial for selecting the most suitable approach for software testing in a given scenario.

Now let us go through the various software testing models and their benefits:

#1. Waterfall Model

This is the most basic software development life cycle process, which is broadly followed in the industry. Here, the developers follow a sequence of processes where the processes flow progressively downward towards the ultimate goal. It is like a waterfall where there are a number of phases.

These phases each have their own unique functions and goals. There are, in fact, four phases: requirement gathering and analysis phase, software design, programmed implementation and testing, and maintenance. All these four phases come one after another in the given order.

In the first phase, all the possible system requirements for developing a particular software are noted and analyzed. This, in turn, depends on the software requirement specifications, which include detailed information about the expectations of the end user. Based on this, a requirement specification.

A document is created that acts as input to the next phase, i.e., the software design phase. What needs to be emphasized here is that once you move into the next phase, it won’t be possible to update the requirements. So you must be very thorough and careful about the end-user requirements.

Advantages

• Easy to implement and maintain.
• The initial phase of rigorous scrutiny of requirements and systems helps save time later in the developmental phase
• The requirement for resources is minimal, and testing is done after the completion of each phase.

Disadvantages

• It is not possible to alter or update the requirements
• You cannot make changes once you are in the next phase.
• You cannot start the next phase until the previous phase is completed

#2. V Model

This model is widely recognized as superior to the waterfall model. Here, the development and test execution activities are carried out side by side in a downhill and uphill shape. In this model, testing starts at the unit level and spreads toward integration of the entire system.

So, SDLC is divided into five phases – unit testing, integration testing, regression testing, system testing, and acceptance testing.

Advantages

• It is easy to use the model since testing activities like planning and test design are done before coding
• Saves time and enhances the chances of success.
• Defects are mostly found at an early stage, and the downward flow of defects is generally avoided

Disadvantages

• It is a rigid model
• Early prototypes of the product are not available since the software is developed during the implementation phase
• If there are changes in the midway, then the test document needs to be updated

#3. Agile model

In this SDLC model, requirements and solutions evolve through collaboration between various cross-functional teams. This is known as an iterative and incremental model.

Also Read:  Selenium Tutorial For Beginners- An Overall View into this Tool.

Advantages

• Ensure customer satisfaction with the rapid and continuous development of deliverables.
• It is a flexible model as customers, developers, and testers continuously interact with each other
• Working software can be developed quickly, and products can be adapted to changing requirements regularly

Disadvantages

• In large and complex software development cases, it becomes difficult to assess the effort required at the beginning of the cycle
• Due to continuous interaction with the customer, the project can go off track if the customer is not clear about the goals

#4. Spiral model

It is more like the Agile model, but with more emphasis on risk analysis. It has four phases: planning, risk analysis, engineering, and evaluation. Here, the gathering of requirements and risk assessment is done at the base level, and every upper spiral builds on it.

Advantages

• Risk avoidance is enhanced due to the importance of risk analysis.
• Its a good model for complex and large systems.
• Depending on the changed circumstances, additional functionalities can be added later on
• Software is produced early in the cycle

Disadvantages

• Its a costly model and requires highly specialized expertise in risk analysis
• It does not work well in simpler projects

#5. Rational Unified Process

This model also consists of four phases, each of which is organized into a number of separate iterations. The difference with other models is that each of these iterations must separately satisfy defined criteria before the next phase is undertaken.

Advantages

• With an emphasis on accurate documentation, this model is able to resolve risks associated with changing client requirements.
• Integration takes less time as the process goes on throughout the SDLC.

Disadvantages

• The biggest disadvantage is that the team members must be experts in their niche.
• In big projects such as continuous integration, it might give rise to confusion

#6. Rapid application development

This is another incremental model, like the Agile model. Here, the components are developed parallel to each other. The developments are then assembled into a product.

Advantages

• The development time is reduced due to the simultaneous development of components, and the components can be reused
• A lot of integration issues are resolved due to integration from the initial stage

Disadvantages

• It requires a strong team of highly capable developers with individual efficacy in identifying business requirements
• It is a module-based model, so systems that can be modularized can only be developed in this model
• As the cost is high, the model is not suitable for cheaper projects

#7 Iterative Model

The iterative model does not require a complete list of requirements before the start of the project. The development process begins with the functional requirements, which can be enhanced later. The procedure is cyclic and produces new versions of the software for each cycle. Every iteration develops a separate component in the system that adds to what has been preserved from earlier functions.

Advantages

• It is easier to manage the risks since high-risk tasks are performed first.
• The progress is easily measurable.
• Problems and risks that are labeled within one iteration can be avoided in subsequent sprints.

Disadvantages

• The iterative model needs more resources compared to the waterfall model.
• Managing the process is difficult.
• The final stage of the project may not entirely determine the risks.

#8 Kanban Model

The Kanban Model is a visual and flow-based approach to software development and project management. It relies on a visual board to represent work items, which move through different process stages. These stages include backlog, analysis, development, testing, and deployment.

Each work item in a Kanban system has a card on the board to represent it, and team members move these cards through the stages as they complete them.

The board provides a real-time visual representation of the work in progress and helps teams identify bottlenecks or areas for improvement.

Continuous improvement is a key principle of Kanban. Teams regularly review their processes, identify areas of inefficiency, and make incremental changes to enhance workflow. This adaptability and focus on improvement make the Kanban Model well-suited for projects with evolving requirements and a need for continuous delivery.

Advantages of Kanban Model:

• Visual Representation: Provides a clear visual overview of work items and their progress.
• Flexibility: It is adaptable to changing priorities and requirements, making it suitable for dynamic projects.
• Continuous Improvement: Encourages regular process reviews and enhancements for increased efficiency.
• Reduced Waste: Minimizes unnecessary work by focusing on completing tasks based on actual demand.

Disadvantages of the Kanban Model:

• Limited Planning: Less emphasis on detailed planning may be a drawback for projects requiring extensive upfront planning.
• Dependency on WIP Limits: Ineffective management of work-in-progress (WIP) limits can lead to bottlenecks.
• Complexity Management: This may become complex for large-scale projects or those with intricate dependencies.
• Team Dependency: This relies on team collaboration and communication, which can be challenging if not well coordinated.

#9 The Big Bang Model

• No Formal Design or Planning: The Big Bang Model is characterized by an absence of detailed planning or formal design before the development process begins.
• Random Testing Approach: Testing is conducted randomly, without a predefined strategy or specific testing phases.
• Suitable for Small Projects: This model is often considered suitable for small-scale projects or projects with unclear requirements.

Advantages of the Big Bang Model:

1. Simplicity: The model is simple and easy to understand.
2. Quick Start: Quick initiation, as there is no need for elaborate planning.

Disadvantages of the Big Bang Model:

1. Uncertainty: Lack of planning and design can lead to uncertainty and chaos during development.
2. Testing Challenges: Random testing may result in inadequate test coverage, and missing critical issues.
3. Limited Scalability: Not suitable for large or complex projects due to a lack of structured processes.

#10 Scrum Model

 Conclusion

In conclusion, the diverse landscape of software testing models within the Software Development Life Cycle (SDLC) offers a range of options to cater to different project requirements and complexities.

From traditional approaches like the waterfall model to more adaptive frameworks like Scrum and Kanban, each model brings its own set of advantages and disadvantages.

The choice of a testing model is crucial, influencing factors such as early issue detection, project adaptability, and overall software quality. As technology evolves, so does the array of testing methodologies, ensuring that software development stays dynamic and responsive to the ever-changing needs of the industry.

BVA (Boundary Value Analysis) is a software testing technique that focuses on testing values at the extreme boundaries of input domains. It is based on the observation that defects frequently occur on the outskirts of valid input ranges rather than in the center. Testers hope to identify potential issues and errors more effectively by testing boundary values. BVA is widely used in black-box testing and is especially useful for detecting off-by-one errors and other boundary-related issues.

Here’s an example of Boundary Value Analysis:

Consider the following scenario: You are testing a software application that calculates discounts for online purchases. The application provides discounts based on the amount of the purchase and has predefined discount tiers.

• Tier 1: 0% discount for purchases less than $10.
• Tier 2: 5% discount for purchases from $10 (inclusive) to $50 (exclusive).
• Tier 3: 10% discount for purchases from $50 (inclusive) to $100 (exclusive).
• Tier 4: 15% discount for purchases of $100 or more.

In this scenario, you want to apply Boundary Value Analysis to ensure the discount calculation works correctly. Here are the boundary values and test cases you would consider:

• Boundary Value 1: Testing the lower boundary of Tier 1.
  • Input: $9.99
  • Expected Output: 0% discount
• Boundary Value 2: Testing the upper boundary of Tier 2.
  • Input: $10.00
  • Expected Output: 5% discount
• Boundary Value 3: Testing the lower boundary of Tier 3.
  • Input: $50.00
  • Expected Output: 10% discount
• Boundary Value 4: Testing the upper boundary of Tier 3.
  • Input: $100.00
  • Expected Output: 10% discount (Tier 3)
• Boundary Value 5: Testing the lower boundary of Tier 4.
  • Input: $100.01
  • Expected Output: 15% discount
• Boundary Value 6: Testing the upper boundary of Tier 4.
  • Input: $1,000.00
  • Expected Output: 15% discount (Tier 4)

By testing these boundary values, you ensure that the software handles discounts at the tier’s edges correctly. If there are any flaws or issues with the discount calculation, this technique will help you find them. Boundary Value Analysis improves software robustness and reliability by focusing on critical areas where errors are likely to occur.

What are the types of boundary value testing?

Boundary value testing is broadly classified into two types:

Normal Boundary Value Testing: This type is concerned with testing values that are precisely on the boundary between valid and invalid inputs. Normal boundary value testing, for example, would examine inputs like 1, 100, and any values in between if an input field accepts values between 1 and 100.

Robust Boundary Value Testing: This type of testing includes values that are slightly outside of the valid boundary limits. Using the same example, robust boundary value testing would use test inputs such as 0, 101, -1, and 101 to see how the system handles them.

While these are the two most common types of boundary value testing, there are also variations and combinations based on the specific requirements and potential risks associated with the software being tested.

What is the difference between boundary value and equivalence testing?

The goal of boundary value testing is to discover issues related to boundary conditions by focusing on values at the edges of valid ranges. Equivalence testing, on the other hand, groups inputs into equivalence classes in order to reduce the number of test cases while maintaining effective test coverage. Both techniques are useful and can be used in tandem as part of a comprehensive testing strategy.

Advantages and DIsadvantages of Boundary Value Analysis

Benefits of Boundary Value Analysis:

• BVA focuses on the edges or boundaries of input domains, making it effective at identifying issues related to these critical points.
• It provides comprehensive test coverage for values near the boundaries, which are often more likely to cause errors.
• BVA is simple to understand and implement, making it suitable for both experienced and inexperienced testers.
• It can detect defects in the early stages of development, lowering the cost of later problem resolution.

The following are the disadvantages of boundary value analysis:

• BVA’s scope is limited to addressing boundary-related defects and potentially missing issues that occur within the input domain.
• Combinatorial Explosion: BVA can result in a large number of test cases for systems with multiple inputs, increasing the testing effort.
• Overlooking Class Interactions: It fails to account for interactions between different input classes, which can be critical in some systems.
• BVA makes the assumption that system behavior near boundaries is linear, which may not be true for all applications.
• BVA may not cover all possible scenarios or corner cases: While it is effective in many cases, BVA may not cover all possible scenarios or corner cases.

FAQs

What’s boundary value analysis in black box testing with an example

BVA is a black-box testing technique that is used to test the boundaries of input domains. It focuses on valid and invalid input ranges’ edges or boundaries to test values. The primary goal is to ensure that a system correctly handles input values at its limits, as this is frequently where errors occur.

Here’s an illustration of Boundary Value Analysis:

Consider the following scenario: You are testing a simple calculator application, and one of its functions is to add two numbers. The application accepts integers from -100 to +100.

Boundary Values: The following are the boundary values in this scenario:

Lower Boundary: -100 Upper Boundary: +100 BVA Test Cases:

Test with the smallest valid input possible:

Input 1: -100
Input 2: 0
-100 is the expected outcome. (At least one valid input)
Test with the most valid input possible:

Input 1: 100
Input 2: 50
150 (Maximum valid input) is the expected result.
Just below the lower boundary, perform the following test:

Input 1: -101
Input 2: 50
Expected Outcome: Error (outside of the valid range)
Just above the upper limit, perform the following test:

Input 1: 101
Input 2: 50
Error (outside valid range) is the expected outcome.
By using Boundary Value Analysis in this example, you ensure that the calculator application handles edge cases at the input range’s minimum and maximum boundaries, as well as values just outside the boundaries, correctly. This assists in identifying potential boundary value errors or issues.

Equivalence Partitioning and Boundary Value Analysis, What’s the difference?

Both Equivalence Partitioning and Boundary Value Analysis are valuable black-box testing techniques, and the choice depends on the specific characteristics of the input data and where potential issues are expected to arise.