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Effective test-data generation using the modified black widow optimization algorithm

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Abstract

Software testing is one of the software development activities and is used to identify and remove software bugs. Most small-sized projects may be manually tested to find and fix any bugs. In large and real-world software products, manual testing is thought to be a time and money-consuming process. Finding a minimal subset of input data in the shortest amount of time (as test data) to obtain the maximal branch coverage is an NP-complete problem in the field. Different heuristic-based methods have been used to generate test data. In this paper, for addressing and solving the test data generation problem, the black widow optimization algorithm has been used. The branch coverage criterion was used as the fitness function to optimize the generated data. The obtained experimental results on the standard benchmarks show that the proposed method generates more effective test data than the simulated annealing, genetic algorithm, ant colony optimization, particle swarm optimization, and artificial bee colony algorithms. According to the results, with 99.98% average coverage, 99.96% success rate, and 9.36 required iteration, the method was able to outperform the other methods.

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Data availability

The data relating to the current study is available via the following link: https://drive.google.com/drive/folders/1vfB0XnplA7t771tFUz4aap_kc8j-bek1?usp=sharing.

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Authors and Affiliations

  1. Department of Software Engineering, Faculty of Engineering and Natural Science, Istinye University, Istanbul, Turkey

    Bahman Arasteh & Ali Ghaffari

  2. Department of Computer Engineering, Islamic Azad University, Tabriz Branch, Tabriz, Iran

    Milad Khadir & Sajad Pirahesh

  3. Computer Engineering Department, Faculty of Engineering, Istanbul Topkapi University, 34087, Istanbul, Turkey

    Mahsa Torkamanian-Afshar

  4. Department of Computer Science, Khazar University, Baku, Azerbaijan

    Ali Ghaffari

Authors
  1. Bahman Arasteh
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  2. Ali Ghaffari
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  3. Milad Khadir
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  4. Mahsa Torkamanian-Afshar
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  5. Sajad Pirahesh
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Contributions

The proposed method was developed and discretized by B. Arasteh and M. Khadir. The designed algorithm was implemented and coded by B. Arasteh, M. Khadir and A. Ghaffari The implemented method code was adapted and benchmarked by B. Arasteh and M. Khadir. The data and results analysis were performed by B. Arasteh and A. Ghaffari. The manuscript of the paper was written by B. Arasteh and Mahsa Torkamanian-Afshar and S. Pirahesh.

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Correspondence to Bahman Arasteh.

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The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. The authors have no relevant financial or non-financial conflict of interest.

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The data used in this research does not belong to any other person or third party and was prepared and generated by the researchers themselves during the research. The data of this research will be accessible to other researchers.

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Arasteh, B., Ghaffari, A., Khadir, M. et al. Effective test-data generation using the modified black widow optimization algorithm. SIViP (2024). https://doi.org/10.1007/s11760-024-03236-8

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  • DOI: https://doi.org/10.1007/s11760-024-03236-8

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