API Regression Testing: Importance & Challenges in 2025

Software testers and developers use regression testing to create efficient software programs. Research on the challenges of regression testing shows that it accounts for 80% of the total testing cost, while software testing constitutes 50–60% of the total cost of a project.¹

We examine the importance of regression testing and its implications on API regression testing and weigh the tradeoffs. 

What is regression testing? 

Regression testing examines existing software to ensure every function is intact after an update or addition. It aims to catch errors that might have unknowingly made their way into the updated build of the software.  

Source: Katalon

The growing need for automated API regression testing

The use of application programming interfaces (APIs) increases as 83% of all internet traffic belongs to API-based services.² API testing has maintained its place as a key testing practice in software testing due to this popularity. In addition to API testing, regression testing is needed to check an API-based apps’ functionality after any changes, updates, or improvements. It is one of the known practices to mitigate defects and malfunctions. ³  

Research shows that increasing the automation level of API regression testing and reducing manual efforts has a significant positive impact on the business-critical system.⁴ Such a positive impact can be seen in large-scale business software systems where manual regression testing is difficult, laborious, and time-consuming due to a large number of possible test scenarios. Automating API regression testing allows businesses to reduce such issues to achieve better models.

API regression testing helps ensure that updates to services don’t introduce bugs or break existing functionality. Many teams automate these checks using API testing tools that integrate directly into CI/CD pipelines.

Top 3 benefits of automated regression testing

The benefits of regression testing or automated regression testing regarding a software’s cost, quality, and time to market could be seen as similar to test automation. It is established by thorough research⁵ : that test automation is one of the necessary elements in software development as testing uses:

• Around 40%-50% of total resources,
• 30% of total effort
• 50%-60% of the total cost of software development.

Regression testing is no exception. It provides:

• Consistent stability: Regression testing improves the overall quality and strength of APIs by ensuring the functions of applications are intact. It achieves this via maintaining existing coding functionality or evaluating a new programming code. Maintaining applications’ functions via regression testing helps businesses stay on track.
• Customer growth and confidence: It is estimated that software failures cause companies to lose 2.3 billion of shareholder value on the day of the announcement of the failure. ⁶ . Regression testing’s test cycle allows organizations to demonstrate proficiency by pointing out APIs’ lack of errors. Businesses can be more comfortable and sure about delivering APIs to their customers. 
• Cost-effectiveness: It is costly and inefficient to create test cases each time. By reusing test cases, automated regression testing provides efficiency and allows businesses to maximize their reserved testing. Automated test cases can also reduce cycle time and cost by 50%, and free up employees’ time to concentrate on software development and contribute to product quality.

Security remains a parallel concern during regression testing. Running dedicated scans using API security testing tools can help uncover vulnerabilities introduced during code changes.

Challenges of API regression testing

• High upfront cost. Regression testing may account for 80% of the total testing cost.⁷ However, while regression testing may seem costly, data, research and use cases indicate the benefits of using such a practice in the long run.⁸ : The current trajectory of API regression testing shows numerous benefits, some of which are covered by this article, for businesses and software development teams to use. 
• High frequency of testing. Regression testing is required for every minor modification to a code since a small section of the updated or changed code might cause problems within an API. Businesses can evade this issue by cooperating with the IT team to schedule updates and regression testing to multiple APIs in an organized manner to reduce testing frequency.

Best practices for API regression testing

1. Create Comprehensive Test Cases for Each Endpoint

Ensure that each API endpoint has a clear set of test cases covering all possible input-output combinations, including success scenarios, boundary conditions, and failure cases. Comprehensive test cases reduce the likelihood of missing errors in new versions of the API.

Example: Suppose you have an API endpoint for adding users, which takes user details like name, email, and age. You should create test cases for:

• Valid inputs (e.g., valid name, email, and age)
• Edge cases (e.g., a user with the maximum allowable name length or an empty name field)
• Invalid inputs (e.g., an invalid email format or a negative age)

Maintaining quality over time requires not just good tools but good documentation. Having a clear framework for your test automation documentation ensures consistency and easier onboarding.

2. Automate Regression Tests

Automating regression tests ensures that your test suite is consistently executed for each new version of the API, saving time and reducing human error. Automated tools like Postman, JUnit, or Selenium can help ensure that all endpoints are tested regularly, even in large, complex systems.

Example: After building your API, you can set up a CI/CD pipeline that automatically runs your regression test suite whenever new code is pushed to the repository. This helps detect issues immediately and ensures that previous functionality hasn’t been broken by recent changes.

As teams scale their test coverage, they often adopt AI-driven methods to boost efficiency. The use of machine learning in test automation is one example of how modern approaches are reducing manual workload and improving accuracy.

3. Maintain a Well-Defined Baseline

Maintain a set of baseline tests that reflect the current state of your API’s expected behavior. These tests should be updated only when changes to the API are made deliberately. Having this baseline ensures that the tests focus on catching regressions rather than irrelevant changes.

Example: If you add a new field, such as phone_number, to the user creation endpoint, update the baseline test cases to include tests for this new field. If any new test failures occur that are unrelated to phone_number, you can more easily identify that they’re regressions rather than expected changes.

Security is also a key concern during regression testing. Techniques like API fuzz testing can be used to detect vulnerabilities by sending unpredictable or malformed inputs.

4. Mock External Dependencies

When testing APIs that rely on external services (e.g., payment gateways, third-party databases), it’s important to mock those services to make your tests faster, more reliable, and deterministic. Mocking external services also allows you to test scenarios that may be difficult to reproduce in a real environment, such as timeouts or unexpected responses.

Example: If your API interacts with a payment gateway like Stripe, instead of making actual API calls to Stripe during regression testing, use a mocking framework to simulate Stripe’s responses. This ensures your API functions correctly when the third-party service responds as expected or throws an error.

5. Version Control Your Test Data

To ensure consistent results, version-control both your API test scripts and the data you use in testing. This allows you to roll back to previous versions of the test data and scripts if needed and ensures that the test data evolves in line with the API itself. Version control tools can help you with that.

Example: Suppose you are testing an API for a library management system, and your test data contains information on books, authors, and borrowers. By versioning this data, you ensure that if the API changes and introduces new fields (e.g., book genre), you can easily track how and when test data changed to accommodate this. It also helps maintain consistency across test environments.

External Links

• 1. API Message-Driven Regression Testing Framework.
• 2. 20 Impressive API Economy Statistics | Nordic APIs |.
• 3. Mehvish Rashid “Evaluating the Effectiveness of Regression Testing”
• 4. API Message-Driven Regression Testing Framework.
• 5. Divya Kumar, K.K. Mishra,”The Impacts of Test Automation on Software’s Cost, Quality and Time to Market”Procedia Computer Science
• 6. What is the real cost of quality? | PPT. Slideshare
• 7. API Message-Driven Regression Testing Framework.
• 8. ScienceDirect.