Data Analytics: Predicting Software Bugs in Industrial Products

Robert Hanmer () and Veena Mendiratta ()
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Robert Hanmer: Nokia
Veena Mendiratta: Northwestern University

A chapter in System Dependability and Analytics, 2023, pp 39-53 from Springer

Abstract: 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.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ssrchp:978-3-031-02063-6_3

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DOI: 10.1007/978-3-031-02063-6_3

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