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Tri-level regression testing using nature-inspired algorithms

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Abstract

A software needs to be updated to survive in the customers’ ever-changing demands and the competitive market. The modifications may produce undesirable changes that require retesting, known as regression testing, before releasing it in the public domain. This retesting cost increases with the growth of the software test suite. Thus, regression testing is divided into three techniques: test case prioritization, selection, and minimization to reduce costs and efforts. The efficiency and effectiveness of these techniques are further enhanced with the help of optimization techniques. Therefore, we present the regression testing using well-known algorithms, genetic algorithm, particle swarm optimization, a relatively new nature-inspired approach, gravitational search algorithm, and its hybrid with particle swarm optimization algorithm. Furthermore, we propose a tri-level regression testing, i.e., it performs all the three methods in succession. Nature-inspired algorithms prioritize the test cases on code coverage criteria. It is followed by selecting the modification-revealing test cases based on the proposed adaptive test case selection approach. The last step consists of the removal of redundant test cases. The hybrid algorithm performed well for the average percentage of statement coverage, and the efficiency of genetic algorithm and particle swarm optimization is better comparatively. The proposed test case selection method can select at least 75% modification-revealing test cases using nature-inspired algorithms. Additionally, it minimizes the test suite with full statement coverage and almost negligible fault coverage loss. Overall, the simulation results show that the proposed hybrid technique outperformed the other algorithms.

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Acknowledgements

The University Grants Commission supports this work under the JRF-NET scheme with Reference Number 3469/(NET-DEC. 2014). We want to express our deep sense of gratitude to Prof. Saroj Ratnoo for proofreading. We would also like to thank the anonymous reviewers for their valuable comments and consideration.

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  1. Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, Hisar, Haryana, India

    Anu Bajaj & Om Prakash Sangwan

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  1. Anu Bajaj
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Correspondence to Anu Bajaj.

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Bajaj, A., Sangwan, O.P. Tri-level regression testing using nature-inspired algorithms. Innovations Syst Softw Eng 17, 1–16 (2021). https://doi.org/10.1007/s11334-021-00384-9

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