Whenever developers change code, QA teams need to check whether the change has affected existing functionality.
One common approach is to run the entire regression test suite. This helps identify unexpected issues, but as the number of tests grows, execution can take more time.
Running only a few tests may save time, but it can also leave important bugs undetected. So, how can we reduce regression testing time without increasing the risk of missing bugs?
Let's understand with a simple example
Imagine a banking application where users can transfer money between accounts. A developer changes the logic that calculates transfer fees.
The QA team needs to check whether the change affects:
- The transfer fee calculation
- The total amount deducted from the sender's account
- The amount received by the other account
- The transaction history
But should the team also rerun every test related to login, profile settings, and notifications?
The answer depends on how these features are connected to the changed code. Running every test after every small change can waste time. But testing only the modified function might miss problems in other connected areas.
Instead of running the entire test suite every time, teams can use code changes and impact analysis to identify tests that are relevant to the change.
A practical approach includes:
Identify what was modified and which application components might be affected. Use code relationships, test mappings, and impact analysis to find tests associated with the changed areas.
Even if a test isn't directly linked to the modified code, check whether it covers an important business operation, such as transferring money or verifying transaction records.
Run the selected tests and investigate failures using available execution evidence. If dependencies are unclear or a critical flow might be affected, running additional regression tests may be the safer choice.
The goal isn't simply to run fewer tests. It's to make informed decisions about which tests to run and when.
While exploring x360 AI Tech, I found its Smart Test Selection approach relevant to this challenge.
The platform describes selecting relevant tests based on code changes and impact analysis. This can help teams focus their testing efforts on potentially affected areas rather than always running the entire test suite.
However, test selection cannot guarantee that every affected scenario will be identified. Indirect dependencies and critical business flows still need careful consideration.
For me, the important part is balancing execution speed with confidence in the release. Saving testing time is useful only when the team has a sensible way to manage the risk of missing a defect.
When you reduce regression testing after a code change, how do you make sure you're not skipping a test that could catch a critical bug?
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