Lunch seminar: Improving Machine Learning-based Test Case Selection

This week, our doctoral candidate Khaled Al-Sabbagh will present his research about noise selection – which is a preparation for his Licentiate seminar:

Continuous integration is a modern software engineering practice that promotes for continuously integrating and testing new code changes as soon as they get submitted to the project repository. One challenge in continuous integration concerns the ability to select a subset of test cases that have the highest probability of revealing faults during each integration cycle. The availability of large amounts of data about code changes and executed test cases in continuous integration systems poses an opportunity to design data-driven approaches that can effectively select test cases for regression testing.

The objective of this thesis is to create a method for selecting test cases that have the highest probability of revealing faults in the system, given new code changes pushed into the code-base. Using historically committed source code and their respective executed test cases, we can utilize textual analysis and machine learning to design a method, called MeBoTS, that can learn the selection of test cases.
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