An Ant Colony Algorithm to Prioritize the Regression Test Cases of Object-Oriented Programs

Background/Objectives: The limited resources force to choose an effective prioritization technique, which makes an ordering of the test case so that the most suitable test case will execute first. Methods/Statistical Analysis: Regression test case prioritization using Ant colony optimization for the object-oriented program is proposed in this paper. Using the previous version test pool a complete graph is generated. The node of the graph represents test case and their fault exposing potential is used by the ant to select a prioritization sequence. Findings: The effectiveness of the test case ordering is measured using APFD (Average percentage of Faults Detected). Experiments on three object oriented subject programs are performed to judge the said approach. The empirical results indicate that the algorithm implemented using ant colony optimization gives higher APFD value than the random techniques. Applications/Improvements: This technique may be used by the quality assurance team for prioritizing test case as it space and time complexity is less as compared to random ordering.