Employing rule mining and multi-objective search for dynamic test case prioritization

Test case prioritization (TP) is widely used in regression testing for optimal reordering of test cases to achieve specific criteria (e.g., higher fault detection capability) as early as possible. In our earlier work, we proposed an approach for black-box dynamic TP using rule mining and multi-objective search (named as REMAP) by defining two objectives (fault detection capability and test case reliance score) and considering test case execution results at runtime. In this paper, we conduct an extensive empirical evaluation of REMAP by employing three different rule mining algorithms and three different multi-objective search algorithms, and we also evaluate REMAP with one additional objective (estimated execution time) for a total of 18 different configurations (i.e., 3 rule mining algorithms × 3 search algorithms × 2 different set of objectives) of REMAP. Specifically, we empirically evaluated the 18 variants of REMAP with 1) two variants of random search while using two objectives and three objectives, 2) three variants of greedy algorithm based on one objective, two objectives, and three objectives, 3) 18 variants of static search-based prioritization approaches, and 4) six variants of rule-based prioritization approaches using two industrial and three open source case studies. Results showed that the two best variants of REMAP with two objectives and three objectives significantly outperformed the best variants of competing approaches by 84.4% and 88.9%, and managed to achieve on average 14.2% and 18.8% higher Average Percentage of Faults Detected per Cost (APFD _c ) scores.