Automatic Test Data Generation for Basis Path Testing

Gagan Kumar; Vinay Chopra

doi:10.17485/IJST/v15i41.1503

Article

Automatic Test Data Generation for Basis Path Testing

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v15i41.1503

Year: 2022, Volume: 15, Issue: 41, Pages: 2151-2161

Original Article

Automatic Test Data Generation for Basis Path Testing

Gagan Kumar^1*, Vinay Chopra²

¹Research Scholar, Computer Science & Engineering, I.K.G. P.T.U Jalandhar, 144603, Punjab, Inda
²Assistant Professor, Computer Science & Engineering, D.A.V. Institute of Engineering & Technology, Jalandhar, 144008, Punjab, India

*Corresponding Author
Email: [email protected]

Received Date:18 July 2022, Accepted Date:23 September 2022, Published Date:03 November 2022

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: This paper presents a new hybrid ACO-NSA algorithm for the automatic test data generation problem with path coverage as an objective function. Method: In it, at the first instance, test data (detectors) are generated with the ant colony optimization algorithm (ACO), and then the generated data set (detector set) has been refined by a negative selection algorithm (NSA) with Hamming distance. Findings: The algorithm’s performance is tested on several benchmark problems with different data types and variables for metrics average coverage, average generations, average time and success rate, Iteration value 1000 is set for average coverage, average generations, average time and 200 for success rate. The obtained results from the proposed approach are compared with some existing approaches. The results are very efficient with high efficacy, higher path coverage, minimal data redundancy, and less execution time. Applications: This approach can be applied in any type of software development process in software engineering to reduce the testing efforts. Novelty: The approach is based on two distinct methodologies: metaheuristic search and artificial immune search, and its fitness is measured using path coverage as the fitness function. The approach provides 99.5% average path coverage, 2.72% average number of generations in 0.07 ns, and 99.9% success rate, which is significantly better than comparable approaches.

Keywords: Test data generation; Metaheuristic search; Artificial immune search; Ant colony optimization; Negative selection algorithm; Path coverage

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Copyright

© 2022 Kumar & Chopra. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)