Regression test analysis allows teams to spot trends in their testing cycle and flag repeat issues.
While some teams might have their own set of checkpoints to determine the strength and value of their regression tests, common measurements can include deployment frequency, change failure rate, lead and cycle time, velocity, and work in progress.
One of the most impactful ways to cut time in your testing process and your regression test analysis is by automating some (or all) of your testing cycle.
Using the data from your test suites, teams can use Launchable’s machine learning model to measure test suite entropy and how their regression testing is performing with deep test suite insights.
One of the worst feelings an engineering and QA team can have is being stuck in an endless testing cycle. It’s a long, sometimes stressful game of playing whack-a-mole with code for days on end just to integrate changes.
Without effective testing, dev teams will quickly drown in the costs of maintaining quality. They’ll face long test suite runtimes and increased machine hours to get it done. This can be remediated with regression testing, but that quickly can fall into its own pit trap of endless testing cycles if not done correctly.
But it doesn’t need to be this way. Teams that take the information they get from regression testing and track their progress throughout the testing cycle can make massive improvements to production - we call this regression test analysis.
Regression testing assesses the entirety of a piece of software after changes or updates have been made. Whenever a new feature, improvement, or modification is made, regression tests help ensure that everything is still working properly afterward.
However, regression tests are often incredibly resource-intensive. Not only do they take a long time to run, but they also consume machine hours. The expense of machine hours grows larger as testing goes on, as more issues can arise while fixing existing ones, which is where regression testing analysis comes in.
Once a cycle of regression testing has been done, teams should take the time to analyze the data they’ve received in order to improve their tests in the future. Regression test analysis allows teams to spot trends in their testing cycle and flag repeat issues. Then, they can take the time to work on testing only the parts that are still causing errors while redirecting efforts away from testing parts of an application that have already passed.
Regression test analysis assesses the efficiency and efficacy of your regression tests. While some teams might have their own set of checkpoints to determine the strength and value of their regression tests, common measurements can include:
Deployment frequency: Assesses how often the team successfully releases software into production. If this number is high, it’s a strong indication that the process is running smoothly, but if we see high deployment frequency AND high
Change failure rate: Likely a top measure, it tracks how many deployments lead to a degradation in service that must be addressed. Other speed-related metrics don't address some of these issues. While it may be possible to deploy software quickly, this metric helps indicate if it’s sustainable or of high quality.
Lead time and cycle time: Consider these two as quality indicators of how long the software development process is taking on a daily basis. A project's lead time is the time between when a project starts and when it is delivered to the customer, whereas the cycle time reveals the length of time it takes to complete each individual project.
Velocity: Specifically meant for agile teams, velocity indicates how long it took a team to finish a particular sprint. However, velocity does not accurately show the effort it took to keep up with this timeline.
Work in progress (WIP): Tallies the number of regression tests in progress. If this measurement is high, it’s a good indicator of bottlenecks, but simply knowing the tally of WIP doesn’t fix it. Teams need to be able to intelligently select the critical regression tests to run, and remove the remaining regression test bloat to make the throughput most efficient.
The above metrics might shed light on elements of regression testing health and efficiency, but they don’t give you the complete picture and where and how to optimize your testing further. This can lead teams to include exploratory testing within their regression testing.
Exploratory testing can be a helpful approach when assessing regression tests, but it is a drain on developer resources as it is a manual process. Intelligently analyzing regression tests reduces the need for exploratory testing and allows developer resources to focus their skills on more critical parts in the SDLC.
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Regression testing should be performed whenever there is a new update being made to a codebase. Whether it’s a minor feature, improvements, or new functionality that has been added, regression tests should happen to ensure nothing breaks once it’s in production.
This means that regression testing analysis should be performed at this time, too. After every cycle of regression testing, teams should analyze the data and use it to improve the next cycle. With proper regression testing analysis, teams can dramatically improve their short-term and long-term testing cycles.
Related Article: Guide to Faster Software Testing Cycles
With regression testing analysis, your teams can dramatically improve their testing cycles. Being able to spot problem areas and focusing on more testing there is a boon on its own. Teams can track how many new issues appear between tests and see what parts are consistently passing their tests each time.
QA teams can also use regression testing analysis to see what different regression testing methods are more efficient than others and can track how many issues arise during various steps of the testing cycle. Additionally, teams can stop spending resources on parts of a codebase that consistently pass all tests, allowing them to pass those parts to production.
One of the most impactful ways to cut time in your testing process and regression test analysis is by automating some (or all) of your testing cycle. With automation at the forefront, you can save hours of time, effort, and money during your testing cycle. And Launchable is here to take your tests to the moon.
Regression test analysis includes analyzing the health of your test suite over time. Using the data from your test suites, teams can use Launchable’s machine learning model to measure test suite entropy and how their regression testing is performing with deep test suite insights.
With Launchable’s test suite insights, your team can perform regression test analysis by:
✔️ Identifying if there has been an increase in test session time. This could imply that the regression testing cycle time has been spiking up with Test Session Duration Insights.
✔️ Tracking which tests fail most often. An increase in Test Session Failure Ratio means that a release is becoming unstable, especially if the bump is in post-merge tests.
✔️ Assessing if your tests are returning accurate results, or if they are sending back false negatives and positives with Flaky Tests Insights.
With the right data-driven insights, regression test analysis can help connect the dots between failed regression tests and how they are affecting your test runtime and deployment velocity.
Launchable gives a clear look at the effectiveness of your regression tests and helps you analyze where you can improve them. Empower your team to run intelligent tests that enable you to ship with more confidence. Use Launchable to assess the health of your test suite and dynamically reorder your tests and focus on what matters most.