Test case prioritization using machine learning for large-scale continuous integration environment

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Test prioritization and selection is of great importance in test process optimization to make efficient use of testing resources, especially when it comes to regression testing for different code submissions in the continuous integration of DevOps practice. Appropriate knowledge processing and learning techniques to deal with numerous and versatile sources of information can be used to address efficiently the peculiarities of each code submissions as well as software baselines and evaluate their quality characteristics. The cognitive test system in IBM has a long term goal to look at how to use machine learning and AI techniques to streamline and intelligently adapt testing environment to align with the changes and risks being introduced with each code change. A number of key contributions in this research will include: 1) developing a strategy that can automatically select the test suites that could be impacted by a change and identify the most likely ones to fail based on a given change, (2) modeling the size, pervasiveness and scope of a change and verify that by determining an optimal set of test suites to execute, and (3) using machine learning to model a developers skill in changing a given area and adjust test strategy accordingly.