Implementation and Evaluation of an Automatic Recommender for Integration Test Cases

Abstract

Continuous integration promises advantages by enabling software developing organizations to deliver new functions faster. However, implementing continuous integration, especially in large software development organizations, is challenging because of organizational, social, and technical reasons. One of the technical challenges is the ability to rapidly prioritize the test cases which can be executed quickly and trigger the most failures as early as possible. This thesis propose an automatic recommender based on mining correlations between outcome of test and source code changes. The information retrieval measures recall, precision and f-measure, as well as Matthews correlation coefficient(MCC), as the priority metric in determining this correlations. The founding of this correlations can be used to select and execute the recommended test cases instead of a full regression test case, in order to support the decision processes about which test case that should be executed during the integration cycles to get as short feedback loops as possible.