Regression testing, which evaluates the impact of new code changes against existing code, is critical to releasing a successful software product. While regression testing ensures user quality and performance demands are met, it’s a form of software testing that can be exceptionally slow and frustrating for developers.
That’s where the difference in automated and manual regression testing velocity cycles really comes to play. Manual regression testing can be a tedious and time-sucking process. In particular, the task of maintaining and updating test suites to reflect increasingly complicated changes can overtax and underwhelm devs tired of the routine nature of manual regression testing.
Automated regression testing using machine learning, serves to speed up the velocity of regression testing, while also ensuring testers can maintain a high level of confidence that errors in functionality or continuity after code change will always be uncovered.
Machine learning is used throughout the software testing life cycle (and the software development life cycle) to reduce the time and human effort spent on repetitive, routine tasks and regression tests.
Machine learning algorithms are trained on data to recognize patterns and make predictions or decisions, and can be applied to various aspects of software testing, including regression testing, functional testing, and performance testing.
For regression testing specifically, machine learning helps teams to analyze past test results to identify patterns. Once identified, this information can be used to predict which tests are most likely to fail in the future.
Machine learning can help to automate the regression testing process of identifying bugs, errors, and flaws in a software project. This can help free up the mental space, time, and effort of software testers to focus on more complex (and interesting!) tasks.
For functional testing specifically, machine learning can also be used to quickly and automatically generate test cases, based on the functionality of the software, which also improves the coverage and efficacy of functional testing. Additionally for performance testing, machine learning can be used to analyze operational data and highlight the patterns and trends that may indicate potential performance issues. Through machine learning, teams can also predict the bottlenecks in the regression testing cycle, before testing time.
The bottom line: Machine learning in regression testing can improve the efficiency and effectiveness of the testing process, as well as improve developer experience.
If you’re looking to implement machine learning into your regression testing cycles, there’s four standard steps to make the shift.
Gathering and preprocessing data
Training a machine learning model
Testing and validating the model
Deploying the model in the regression testing process
First, it’s critical to gather, curate, and preprocess the data that will be used to train an ML model. An effective ML model will reduce the need for the repetitive, routine aspects of regression testing and instead automate this process, saving time and effort on behalf of devs. But it’s only as helpful as the data going into the model. This first step is critical for a successful adopting of machine learning.
Next, the machine learning model needs to be trained on your data sets, which will be divided into the training data and the validation data. Training data will train the model to recognize patterns, as well as unusual or surprising components or changes. The validation data set will be used to validate the model during and after testing. Once the model has been trained and tested, it’s time to actually deploy the ML model into the regression testing process.
Due to its nature, regression testing using machine learning can be a complex process to configure, especially if you’re developing your own ML model. But for many DevOps teams, the hours saved with automated regression testing are worth the initial complexity of implementation.
Automated regression testing through machine learning brings big wins for teams focused on efficiency, affordability, and user-friendliness. Using machine learning to automate your regression testing comes with major benefits. Automating regression testing will save your team major hours and mental effort.
With this increased efficiency and accuracy from regression testing using machine learning, there’s a lot more time for innovation and room for focus on achieving objectives that push bigger organizational goals forward - not just daily tasks.
Regression testing using machine learning can also directly impact cost savings. Manual regression testing requires dev time and effort (and may still come along with human errors!), while automated regression testing using machine learning reduces risks and time spent on testing. For organizations, all that saved time can create significant savings over the long haul.
Machine learning can also reduce the complexity of regression testing implementation. A great automated regression testing platform or tool will be easier to introduce and maintain and also offer scalability as a project (and subsequent test suite) grows.
Of course, implementing regression testing using machine learning always comes with the possibility of introducing bias during model training. However, by focusing on best practices during training, like improving data quality, and providing a diverse, varied set of inputs during model training, bias can be sidestepped and avoided.
If you’re looking to further enhance automating regression testing using machine learning, it’s important to consider what the right tool is for you and your organization.
Not all machine learning tools for regression testing are created equal. The best tools offer flexibility, reliability, and instill high confidence in devs. Regression testing using machine learning is the best way for your team to eliminate the complexity of implementation, the ongoing costs, and the frustratingly slow test cycles of manual regression tests.
By selecting the tests with the highest probability of failing, Predictive Test Selection helps teams decide on the best regression tests to run or skip, ultimately slashing the size of your bloated regression test suite. The machine learning model to automatically determine which tests are most critical to run, based on your code changes, and the predicted impact of those oncoming changes.
If you’re ready for a reliable machine learning model to help rev up your regression test selection and cycle times, Launchable can help you to increase efficiency and accuracy, while also slashing the hours spent on regression test selection.