What is Regression Testing? Automated Regression Testing Explained

Introduction

One question that haunts every release: "Did we break something that was already working?"

Regression testing is your defense against unintended consequences, ensuring new code changes don't break existing functionality. From manual testing to automation strategies, this guide provides the techniques, tools, and best practices to streamline regression testing processes that keep software stable, reliable, and production-ready.

What Is Regression Testing?

Regression testing is the process of re-running existing tests to ensure that new code changes do not break any existing functionality. It is usually run after bug fixes, product enhancements, or new features.

Regression testing helps QA teams catch bugs that pop up when code is updated and ensures the software remains stable and reliable across releases. A bug found during regression testing is called a regression.

Regression Testing Example

Consider a banking application that recently introduced a new mobile payment feature. While this feature works well in isolation (unit test), it could unintentionally impact existing areas like fund transfers, balance updates, or transaction history.

The QA team runs a regression test suite to re-check these critical flows.

• Transfers still update balances correctly
• Payment confirmations display without delays
• Transaction logs remain intact after new updates

By catching issues early, regression testing ensures that adding new features doesn't break core functionality that customers rely on.

Why Regression Testing Is Important?

Types Of Regression Testing

The different types of regression testing that are commonly used in software testing:

1. Corrective Regression Testing

It helps ensure the existing code works correctly and checks if the current test cases are usable. When the test results turn positive, QAs can be confident that the test cases are effective and up-to-date. It also allows QA to proceed with test planning and prioritization while incorporating new code changes into the testing process.

2. Selective Regression Testing

It is performed to observe how the code behaves when new code is added to the program. Here, a subset of test cases is selected based on the impacted areas of the software that have been modified.

3. Progressive Regression Testing

It is a testing approach where QAs recognize that code changes may require changes to the test suites. So, they will work on updating the test scripts to align with the new requirements. This method is used when a change affects the product vision.

4. Complete Regression Testing

It is used when numerous updates are performed on the existing code or when the new updates have a distinct impact on the overall software.

5. Partial Regression Testing

It helps identify issues that may arise when newly written code is integrated into existing code. It ensures that the application's performance has not been impacted by the new code.

6. Unit Regression Testing

It is conducted in isolation and mainly focuses on code units without any dependencies or interactions.

7. Visual Regression Testing

It checks for unintended changes in a software application's UI after updates or code changes. It compares screenshots of the current build with a baseline to catch layout shifts, styling issues, or broken visuals. Testsigma supports visual regression testing, making it easier for testers to identify visual bugs.

What Are The Techniques Of Regression Testing?

There are several regression testing techniques that can be applied based on the nature of the software and the changes being made. Here are some commonly used techniques of regression testing,

1. Retest All

This technique involves re-executing the entire set of existing test cases. This consumes huge time and resources and that is why it is highly expensive.

2. Regression Test Selection

In this method, testers select specific test cases from the test suite for the feature that has to be retested based on the changes made to the code. The test cases are categorized as reusable and obsolete. Reusable test cases can be used again in future regression cycles, while obsolete test cases are no longer used in upcoming regression cycles.

3. Test Case Prioritization

Instead of re-executing all test cases, this technique focuses on identifying and prioritizing the most critical test cases.

4. Hybrid

This is a combination of Regression Test Selection and Test Case Prioritization methods. That is, instead of selecting the entire test suite, select the test cases that are re-executed based on their priority.

Challenges of Manual Regression Testing

The most common challenges companies face when they perform regression tests manually:

Time Consuming

It is highly time consuming and resource extensive, especially as applications grow in complexity. Each regression cycle requires testers to manually execute test cases that may have been run multiple times before. This can be risky when teams work under tight deadlines.

Prone To Human Error

Manual testing heavily relies on human attention and consistency, making it prone to errors when done repeatedly. Different testers may run the same test differently, making it hard to achieve consistent results. It becomes challenging to compare outcomes between test runs and to reproduce bugs when found.

Not Scalable

As applications expand, the number of test cases requiring regression testing also grows exponentially. Manual testing struggles to scale efficiently, often resulting in incomplete test coverage.

High Test Maintenance

Keeping test cases updated requires significant effort. When an application's code changes, the respective test cases must be updated manually, which can consume considerable resources and time.

Less Test Coverage

Manual regression testing often focuses on the most commonly used features, potentially missing edge cases.

Not Scalable For Agile Teams

Manual regression testing can struggle to keep pace with agile development methodologies and continuous integration practices. The time required for manual test execution may not align well with rapid development cycles, potentially creating bottlenecks in the release process.

These challenges have led many organizations to explore automated testing allowing manual testers to focus on exploratory testing and strategic testing activities.

What Is Automated Regression Testing?

Benefits Of Automated Regression Testing

Every application must run without errors, ideally all the time. Yet regression bugs occur when new updates take place, which need to be identified with thorough and consistent testing. Manual testing only adds to the cost and time. That's when regression test automation comes to the forefront. Performing regression testing using automation tools helps with the following:

• Cost-effective:Though an initial cost in setting up the tool is involved, automation reduces manual effort, cutting long-term QA costs.
• Reusability of test cases:Reusing automated tests across multiple releases reduces cost, time, and manual efforts.
• High test consistency and accuracy:Automating regression tests eliminates human errors and improves the reliability of test results.
• Achieve high test coverage:Validate thousands of test scenarios across various browsers, devices, and platforms consistently.
• Resource optimization:With automation in place to handle repetitive tasks, QA teams can focus more on complex and exploratory testing tasks.
• Continuous Integration and Delivery (CI/CD):Integrate automated tests into the CI/CD and DevOps pipeline, allowing continuous testing when a build is committed.
• Faster time-to-market:Automation speeds up the release cycle, thereby accelerating the time-to-market.

How To Automate Regression Testing?

A step-by-step guide on getting started with automated regression tests. A Regression Testing strategy would typically look like this,

• Understand the requirements:Determine what kind of tests must be run for your product - front-end, back-end, or both.
• Choose the right regression testing tool:After analyzing the requirements and testing scope, choose the ideal test automation tool that aligns with your testing requirements and business needs.
• Identify and prioritize test cases to automate:Now, identify the test cases that have to be automated and prioritize them as P0s, P1s, P2s, or so.
• Automate test cases:Once you sort the above steps, execute the regression tests using the tool.
• Execute the test cases:After the run is successful, run them locally to check if there are no false failures.
• Prepare Regression Suite:Once they are automated and stable, add them to your regression test suite. You can categorize them as Regression, Smoke, Sanity, etc.
• Test Reporting:After each test run, analyze and share the test reports with the development team for feedback.

Real-World Case Study: Automating Regression Testing at Hansard

Hansard's QA team heavily relied on manual testing, spending 15–20 days per cycle and struggling to accommodate both monthly functional deployments and patch retesting within tight timelines. This manual bottleneck hindered sprint throughput and left little room for agile delivery.

By adopting Testsigma's codeless, AI-driven test automation, Hansard automated 75% of its regression suite and integrated it directly into their CI/CD pipeline. This reduced regression testing from 3 weeks to under 1 week, while sanity test results were available within 30 minutes of code commits.

Automating regression testing with Testsigma turned a major QA roadblock into a scalable, efficient, and reliable process, enabling Hansard to deliver quality on time, every time.

Future of Regression Testing

AI/ML is redefining the software testing space like any other industry. Here's how AI is shaping the future of regression testing:

AI-Driven Test Generation And Maintenance

AI in software testing plays a crucial role in improving testing productivity. AI helps QA teams automatically create and update regression tests as applications evolve. This will significantly reduce test maintenance and flaky tests and ensure that test coverage adapts with each release.

Autonomous AI Testing Agents In Every QA Phase

Intelligent AI agents will work alongside testers, running regression packs, prioritizing test cases, and analyzing failures. They act like AI coworkers, freeing QA engineers to focus on exploratory and strategic testing where manual effort is critical.

Predictive Analytics for Test Prioritization

Machine learning can identify which areas of the product are most likely to break after code changes. It helps QA teams run the most impactful regression tests first, saving time while reducing release risk.

How Testsigma Is Accelerating Regression Tests With AI Agents

The future is already here with Testsigma. At the core of Testsigma are Copilot and Atto, which spin a crew of AI agents who work with QA teams in every stage of the testing lifecycle. A quick look at how Testsigma's AI Testing Agents work at every phase of testing:

• Sprint Planner Agent: Helps plan your tests as soon as a JIRA sprint starts, by automatically detecting it.
• Generator Agent: Instantly turns user workflows into automated test cases using inputs from user requirements, Figma, images, videos, PDFs, docs, prompts, screenshots, and files.
• Optimizer Agent: It continuously refines your tests automatically as the application evolves.
• Runner Agent: Executes test cases up to 10x faster than traditional execution.
• Analyzer Agent: Automatically identifies the root cause of test failures so QAs don′t have to spend time digging through logs and screenshots.
• Bug Reporter Agent: Automatically generates detailed bug reports after every test run.

Testsigma's Capabilities For Automated Regression Testing

Conclusion

Regression testing ensures code changes do not introduce new bugs or break existing functionality. As applications scale, manual regression testing becomes unsustainable due to time constraints and human error. Automated regression testing integrated into CI/CD pipelines provides continuous validation, faster feedback loops, and comprehensive test coverage.

Testsigma enables teams to build stable regression test suites using codeless and AI-driven testing. With features like parallel execution, AI-powered maintenance, and seamless CI/CD integration, Testsigma streamlines regression testing workflows while maintaining high test reliability and coverage.