Regression testing involves re-running test cases against the new build to make sure they still pass.
Sanity testing streamlines the testing process so that developers can quickly work to mitigate the issues discovered with rejected builds.
Sanity testing is a subset of regression testing; the goal of sanity testing is to verify rationality, which should not be confused with smoke testing which verifies stability.
Both regression and sanity tests can cause testing bottlenecks when automated test selection is not a part of the software testing lifecycle.
There are many different aspects of software testing, and it can sometimes be difficult to keep them all straight. Understanding the various types of testing techniques is crucial for implementing the best strategy for improving software quality and increasing development velocity at the same time.
In this post, we’ll take a deep dive into regression testing and sanity testing. We’ll cover the key differences between these testing methods so that you can build the best overall testing strategy for delivering high-quality software.
Sanity testing is a quick evaluation of a software release to ensure it’s eligible for more in-depth testing. After a new code change, the new software version is tested to see if it works as expected. If the sanity test fails, this saves the additional time and money required to thoroughly test a new release.
Regression testing is a form of software testing that ensures an application still works as intended after a new feature is added, a bug is fixed, or there’s any other code change. This reduces the risk of a code change having an unexpected impact on the overall software performance. Regression tests are usually run after a software build has passed sanity testing.
Sanity testing is a subset of regression testing that aims to quickly test a new software version after a code change. It’s essentially a “sanity check” to efficiently determine whether a software release is ready to move forward in the testing process. That means sanity testing is often limited in scope and focuses on clearly defined areas of the application.
The primary benefit of sanity testing is reducing overall software testing effort. Similar to smoke testing, sanity testing is useful for identifying broken builds as early as possible. This helps quality assurance teams quickly reject ineligible software versions before they need to invest in building out an entirely new test suite.
Sanity testing streamlines the testing process so that developers can quickly work to mitigate the issues discovered with rejected builds. The goal of sanity testing is to verify rationality, which should not be confused with smoke testing which verifies stability. By running a select set of test cases, quality assurance teams can determine whether a build is ready to move on to regression testing and other types of in-depth software testing.
Regression testing involves re-running test cases against the new build to make sure they still pass. The name regression testing comes from the word “regress” because it’s aimed at ensuring the new software version works in the same way as its former state.
While there are typically functional tests that analyze the code change itself, this doesn’t help identify any unintended impact on the overall application. Regression testing assesses the broader impact of code changes so that companies can release new features without breaking existing functionality. This is crucial for delivering a positive user experience in the long run.
Regression testing, therefore, is a great way to reduce the risk of code changes and software releases. In turn, this gives developers more confidence to ship new features. Since previous tests are being re-run, regression testing can also reveal if a bug that was supposed to be fixed has reappeared later on. For these reasons, regression testing is a way to guarantee that each new build is an improvement over the previous one.
While regression and sanity testing are very similar techniques, there are a few differences to consider. For one, regression testing is much more in-depth than sanity testing. Sanity testing quickly determines if a code change causes problems, but regression testing involves a large and ever-growing set of tests that analyze overall application performance. That means regression testing is “wide and deep” while sanity testing is “wide and shallow” in scope.
Another important difference is the time and effort involved. As mentioned before, sanity tests are often limited in scope, and they aren’t usually documented either. It’s more of a quick checkpoint before moving on to more testing. However, regression testing involves an in-depth plan to analyze the software extensively using a carefully designed test suite. This makes sanity testing less costly to perform, and it even has the potential to save money as well.
Finally, sanity and regression testing are performed during different stages of the software testing process. Sanity testing is usually the first type of testing that occurs when there are changes to a stable build. After passing sanity testing, the build moves on to functional and regression testing. Quality assurance teams essentially start broadly with sanity testing then move deeper when they’re more confident a build will pass more rigorous testing.
As you can see, there are advantages to implementing both sanity testing and regression testing. Sanity testing can streamline the software testing process, while regression testing can improve overall software quality. However, it’s important to strategically prioritize test cases to prevent bottlenecks during development.
When software is analyzed using sanity testing, regression testing, functional testing, and many other techniques, test suites can grow very large over time. If quality assurance teams don’t find a way to prioritize fewer test cases, the testing process will cause delays and slow software delivery. Automated test selection is the key to balancing test coverage and development velocity.
Launchable is an automated software testing platform built with developers in mind. With the help of machine learning, Launchable’s Predictive Test Selection can assess which tests have the highest probability of failing based on your code and metadata. That way, you can run tests that have the most impact on software quality, and deliver more reliable software faster.