Regression Test Suite Prioritization using Genetic Algorithms

Abstract

Regression testing is an expensive, but important process in software testing. Unfortunately, there may be insufficient resources to allow for the re-execution of all test cases during regression testing. In this situation, test case prioritization techniques aim to improve the effectiveness of regression testing by ordering the test cases so that the most beneficial are executed first. In this paper we propose a new test case prioritization technique using Genetic Algorithm (GA). The proposed technique prioritizes subsequences of the original test suite so that the new suite, which is run within a time-constrained execution environment, will have a superior rate of fault detection when compared to rates of randomly prioritized test suites. This experiment analyzes the genetic algorithm with regard to effectiveness and time overhead by utilizing structurally-based criterion to prioritize test cases. An Average Percentage of Faults Detected (APFD) metric is used to determine the effectiveness of the new test case orderings.