Test Case Optimization for Enhancing System Software Quality using Genetic Algorithm
M. Bharathi1, V. Sangeetha2

1M.Bharathi, Department of Computer Science, Periyar University College of Arts and Science, Pennagaram, Dharmapuri, Tamil Nadu, India.
2Dr.V.Sangeetha, Department of Computer Science, Periyar University College of Arts and Science, Pappireddipatti, Dharmapuri, Tamil Nadu, India. 

Manuscript received on September 16, 2019. | Revised Manuscript received on 26 September, 2019. | Manuscript published on October 10, 2019. | PP: 68-75 | Volume-8 Issue-12, October 2019. | Retrieval Number: L25011081219/2019©BEIESP | DOI: 10.35940/ijitee.L2501.1081219
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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: Software Product Lines (SPLs) embraces an enormous capacity of feature mixtures which cause challenges for evaluating software programs. Testsuite optimization plays major role to develope the quality of SPLs. In combinatorial testing (CT), pair wise fault coverage maximization and test case reduction accomplishes a substantial role for shrinking the testing cost of software programs. Many research works have been developed and designed for CT using different test suite reduction techniques. However Fuzzy clustering and TSRSO techniques do not provide a finest solution for test suite optimization problem. For that, Genetic Algorithm (GA) Technique is recommended and designed for test suite reduction in CT. Metaheuristic genetic algorithm delivers optimum solution in an effective manner. GA chooses and consolidates the testcases in a testsuite based on some principles such that maximum faults covered with minimum execution time. In Proposed GA, finest individuals are nominated for reproduction in order to create descendants of the succeeding generation. In addition, GA is a superior type of evolutionary algorithms generate finest solutions to optimization problems using selection, crossover and mutation operators. Consequently, GA is applied for resolving test suite reduction problem in CT.
Keywords: Software Product Lines, Combinatorial Testing, Test Suite Optimization, Test Cases, Fault Coverage.
Scope of the Article: Algorithm Engineering