An industrial application of test selection using test suite diagnosability

Abstract

Performing full regression testing every time a change is made on large software systems tends to be unfeasible as it takes too long to run all the test cases. The main motivation of this work was to provide a shorter and earlier feedback loop to the developers at OutSystems when a change is made (instead of having to wait for slower feedback from a CI pipeline). The developed tool, MOTSD, implements a multi-objective test selection approach in a C# code base using a test suite diagnosability metric and historical metrics as objectives and it is powered by a particle swarm optimization algorithm. This paper presents implementation challenges, current experimental results and limitations of the developed approach when applied in an industrial context.