Value-Based Test Case Prioritization for Regression Testing Using Genetic Algorithms

Abstract

Test Case Prioritization (TCP) techniques perform better than other regression test optimization techniques including Test Suite Reduction (TSR) and Test Case Selection (TCS). Many TCP techniques are available, and their performance is usually measured through a metric Average Percentage of Fault Detection (APFD). This metric is value-neutral because it only works well when all test cases have the same cost, and all faults have the same severity. Using APFD for performance evaluation of test case orders where test cases cost or faults severity varies is prone to produce false results. Therefore, using the right metric for performance evaluation of TCP techniques is very important to get reliable and correct results. In this paper, two value-based TCP techniques have been introduced using Genetic Algorithm (GA) including Value-Cognizant Fault Detection-Based TCP (VCFDB-TCP) and Value-Cognizant Requirements Coverage-Based TCP (VCRCB-TCP). Two novel value-based performance evaluation metrics are also introduced for value-based TCP including Average Percentage of Fault Detection per value (APFDv) and Average Percentage of Requirements Coverage per value (APRCv). Two case studies are performed to validate proposed techniques and performance evaluation metrics. The proposed GA-based techniques outperformed the existing state-of-the-art TCP techniques including Original Order (OO), Reverse Order (REV-O), Random Order (RO), and Greedy algorithm.