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Interpretable Software Defect Prediction from Project Effort and Static Code Metrics
Version 1
: Received: 15 January 2024 / Approved: 15 January 2024 / Online: 15 January 2024 (14:23:14 CET)
A peer-reviewed article of this Preprint also exists.
Haldar, S.; Capretz, L.F. Interpretable Software Defect Prediction from Project Effort and Static Code Metrics. Computers 2024, 13, 52. Haldar, S.; Capretz, L.F. Interpretable Software Defect Prediction from Project Effort and Static Code Metrics. Computers 2024, 13, 52.
Abstract
Software defect prediction models enable test managers to predict defect-prone modules and assist with delivering quality products. A test manager would be willing to identify the attributes that can influence defect prediction and should be able to trust the model outcomes. The objective of this research is to create software defect prediction models with a focus on interpretability. Additionally, it aims to investigate the impact of size, complexity, and other source code metrics on the prediction of software defects. This research will also assess the reliability of cross-project defect prediction. Well-known machine learning techniques such as support vector machines, k-nearest neighbors, random forest classifiers, and artificial neural networks were applied to publicly available PROMISE datasets. The interpretability of this approach has been demonstrated by SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME) techniques. The developed interpretable software defect prediction models showed reliability on independent and cross-project data. Finally, the results demonstrate that static code metrics can contribute to the defect prediction models, and the inclusion of explainability assists in establishing trust in the developed models.
Keywords
Defect Prediction, Explainable Machine Learning, Software Quality, Interpretability, Cross-Project Defect Prediction
Subject
Computer Science and Mathematics, Software
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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