Open Access Journal of Engineering Applications
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Software Defect Prediction: A Cost-Effective Solution

Imran Andersson Corresponding 0000-0003-3930-4945
University of New South Wales
Shahriar Tran 0000-0003-4899-8450
Ahsanullah University of Science and Technology
Sadia Ahmadi
Ahsanullah University of Science and Technology
31 Mar 2019
pp. 224-250
https://doi.org/10.55555/oajea.2019.4301.014

Abstract

Recent failures in industrial automation have highlighted the need for a more rigorous treatment of software defect prediction. The proposed procedure is demonstrated on a case study representative of industrial automation. The framework classified the outcomes with an accuracy of 20% on the held-out data. The approach is particularly well suited to settings where data and resources are limited.
Keywords: software defect predictionstatistical process controlreinforced concreteblockchain

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