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Time-dependent entropic analysis of software bugs

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Abstract

Software bug analysis and prediction is an important issues for making software successful in this competitive environment. This paper deals with a time-dependent measure of the amount of uncertainty of bug fluctuations in software systems. The time-dependent entropy method (TDEM) uses the sliding window concept where in each window entropy is computed. Shannon and Tsallis entropies are employed on the Bugzilla dataset from open-source Eclipse projects. The bug dataset is divided into several categories which include Automotive, Eclipse Project, IOT Modelling, etc. To characterize local uncertainties in bug fluctuations we make use of time-scaled decisions like budget allocations to debug and provide a better understanding to design an efficient bug-handling system. The graphs are plotted by employing time-dependent entropy on the selected datasets to get insights into the uncertainty of bug fluctuations. It is observed from the results that the entropy distribution of bugs is right skewed which better represents the extremes of the dataset rather than focusing solely on the average.

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This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Computer Science, Ramanujan College, New Delhi, India

    Kamlesh Kumar Raghuvanshi & Arun Agarwal

  2. Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Lucknow, India

    Amit Kumar Singh

  3. School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India

    Khushboo Jain

Authors
  1. Kamlesh Kumar Raghuvanshi
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  2. Arun Agarwal
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  3. Amit Kumar Singh
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  4. Khushboo Jain
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Correspondence to Khushboo Jain.

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Raghuvanshi, K.K., Agarwal, A., Singh, A.K. et al. Time-dependent entropic analysis of software bugs. Int J Syst Assur Eng Manag 14, 1718–1725 (2023). https://doi.org/10.1007/s13198-023-01976-3

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  • DOI: https://doi.org/10.1007/s13198-023-01976-3

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