Bi-Objective Constraint and Hybrid Optimizer for the Test Case Prioritization
K. Senthil Kumar1, A. Muthukumaravel2
1K. Senthil Kumar, Research Scholar, Bharathiar University, Coimbatore, Tamil Nadu
2A. Muthukumaravel, Professor & Head, Department of MCA Bharath University, Chennai, Tamil Nadu.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 3436-3448 | Volume-8 Issue-6, August 2019. | Retrieval Number: F9515088619/2019©BEIESP | DOI: 10.35940/ijeat.F9515.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Regression testing is performed to make conformity that any changes in software program do not disturb the existing characteristics of the software. As the software improves, the test case tends to grow in size that makes it very costly to be executed, and thus the test cases are needed to be prioritized to select the effective test cases for software testing. In this paper, a test case prioritization technique in regression testing is proposed using a novel optimization algorithm known as Taylor series-based Jaya Optimization Algorithm (Taylor-JOA), which is the integration of Taylor series in Jaya Optimization Algorithm (JOA). The optimal test cases are selected based on the fitness function, modelled depending on the constraints, namely fault detection and branch coverage. The experimentation of the proposed Taylor-JOA is performed with the consideration of the evaluation metrics, namely Average Percentage of Fault Detected (APFD) and the Average Percentage of Branch Coverage (APBC). The APFD and the APBC of the proposed Taylor-JOA is 0.995, and 0.9917, respectively, which is high as compared to the existing methods that show the effectiveness of the proposed Taylor-JOA in the task of test case prioritization.
Keywords: Prioritization, Regression testing, Jaya Optimization Algorithm, constraints, and branch coverage.