A Genetic Algorithm for Targeted Regression Testing: Balancing Coverage and Change Focus

Abstract

The selection of regression test cases is used to choose a subset of test suits that are used to exercise the altered program to ensure that the modified part has no unintended consequences on the unmodified part of the program. In previous works, the single objective is used for the selection of test cases. In this thesis, we preserved test case selection as a multi-objective optimization problem. We select code coverage and code change information for test case selection. There are many different methods for test case assortment (i.e., firewall, textual differing g, test-tube, etc.). We used a genetic procedure for multi-objective test case assortment. Our proposed technique first collects the related information such as the size of the system under test, the size of the test suit, and modification between the original and modified versions of the system. Then collect and analyze the code coverage and code change information, also collect user requirements for test case selection. Finally, select a subset of test cases based on cost, fault uncovering, code coverage, and code alteration information for test case selection. The proposed system is for a moderate-level desktop application. Three datasets (Triangle, Tree data structure, and Jodatime) are used for the experiment of the proposed system. For the evaluation of the proposed test selection technique, we used precision and recall evaluation matrices. Our experiment study demonstrates that our proposed technique selects almost 75% of related test cases.