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A sequential ensemble model for software fault prediction

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Abstract

Unlike several other engineering disciplines, software engineering lacks well-defined research strategies. However, with the exponential rise in automation, the demand for software has observed an enormous elevation. Simultaneously, it necessitates having zero failures in the software modules to maximize the availability and optimize the maintenance cost. This has attracted many researchers to try their hand in formalizing the strategies for testing of software. Numerous researchers have suggested various models in this context. The authors in this paper present a sequential ensemble model to predict software faults. The employment of ensemble modeling in software fault prediction is motivated by its competence in various domains. The proposed model is also implemented on the 8 datasets taken from PROMISE and ECLIPSE repository. The proposed model's performance is evaluated using various error metrics, viz. average absolute error, average relative error, and prediction. The obtained results are encouraging and thus establish the competence of the proposed model.

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Authors and Affiliations

  1. Lokmanya Tilak College of Engineering, Navi Mumbai, India

    Monika Mangla

  2. Dr. B.R. Ambedkar National Institute of Technology Jalandhar, Jalandhar, India

    Nonita Sharma

  3. College of Engineering Pune, Pune, India

    Sachi Nandan Mohanty

Authors
  1. Monika Mangla
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  2. Nonita Sharma
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  3. Sachi Nandan Mohanty
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Correspondence to Nonita Sharma.

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Mangla, M., Sharma, N. & Mohanty, S.N. A sequential ensemble model for software fault prediction. Innovations Syst Softw Eng 18, 301–308 (2022). https://doi.org/10.1007/s11334-021-00390-x

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  • DOI: https://doi.org/10.1007/s11334-021-00390-x

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