A Test Case Prioritization Approach Using Genetic Algorithm Guided by the Hypervolume Indicator

<div>Regression testing is performed during maintenance activities to assess whether the unchanged parts of a software behave as intended. To reduce its cost, test case prioritization techniques can be used to schedule the execution of the available test cases to increase their ability of revealing regression faults earlier. Optimal test ordering can be determined using various techniques, such as greedy algorithms and meta-heuristics, and optimizing multiple fitness functions, such as the average percentage of statement and branch coverage. These fitness functions condense the cumulative coverage scores achieved when incrementally running test cases in a given ordering using Area Under Curve (AUC) metrics.</div><div>In this paper, we notice that AUC metrics represent a bi-dimensional (simplified) version of the hypervolume metric, which is widely used in many-objective optimization. Thus, we propose a Hypervolume-based Genetic Algorithm, namely HGA, to solve the Test Case Prioritization problem when using multiple test coverage criteria. Two empirical studies conducted with respect to four state-of-the-art techniques show that (i) HGA is more cost-effective than state-of-the-art approaches, (ii) HGA improves the efficiency of Test Case Prioritization, (iii) HGA is scalable when the number of criteria to consider grows-up.