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Data Analytics: Predicting Software Bugs in Industrial Products

In: System Dependability and Analytics

Author

Listed:
  • Robert Hanmer

    (Nokia)

  • Veena Mendiratta

    (Northwestern University)

Abstract

Achieving high software reliability in products is a costly process. Faults found late in the development cycle are the costliest to fix. Defect prediction models are developed prior to and during various stages of testing to predict the faults remaining or to predict which software modules are more prone to failures. Increasingly machine learning models are used for this purpose, using various code metrics and defect data. In this paper we will review the need for targeted testing and various machine learning approaches for defect prediction. Additionally, we will present a new methodology for improving software reliability during product development based on the results from the analytics models, which we demonstrate with a small case study.

Suggested Citation

  • Robert Hanmer & Veena Mendiratta, 2023. "Data Analytics: Predicting Software Bugs in Industrial Products," Springer Series in Reliability Engineering, in: Long Wang & Karthik Pattabiraman & Catello Di Martino & Arjun Athreya & Saurabh Bagchi (ed.), System Dependability and Analytics, pages 39-53, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-031-02063-6_3
    DOI: 10.1007/978-3-031-02063-6_3
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