A Feature-Driven Framework for Software Fault Prediction
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866916021208612864 |
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| author | Ghazi, Ahmad Nauman Devarapalli, Nagajyothi Javeed, Ashir Alawadi, Sadi Alkhabbas, Fahed AlKharabsheh, Khalid |
| author_facet | Ghazi, Ahmad Nauman Devarapalli, Nagajyothi Javeed, Ashir Alawadi, Sadi Alkhabbas, Fahed AlKharabsheh, Khalid |
| contents | Software fault prediction (SFP) is a critical task in software engineering, enabling early identification of faults in modules to improve software quality and reduce maintenance costs. This research investigates the combined effects of feature selection and parameter tuning on the performance of machine learning (ML) models for SFP. This study evaluates the interaction between feature selection methods, including correlation-based feature selection (CFS), recursive feature elimination (RFE), mutual information (MI), and L1 regularization, where hyperparameter tuning techniques such as grid search, randomized search, and genetic algorithm (GA) are used for optimization of ML algorithms, including random forest (RF), logistic regression (LR), and support vector machines (SVM) for optimized fault prediction performance. The combined application of CFS and GA yielded the highest accuracy, achieving 88.40% with RF, representing an improvement of 18% over baseline models without feature selection or tuning. Feature selection reduced dimensionality and identified critical attributes such as weighted methods per Class (WMC) and coupling between objects (CBO), while iterative parameter tuning optimized model alignment to these feature sets. Notably, the proposed methods demonstrated robustness, with minimal cross-validation variability (+-1.0%), and efficiency, reducing training times in univariate methods such as L1 regularization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_17611 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Feature-Driven Framework for Software Fault Prediction Ghazi, Ahmad Nauman Devarapalli, Nagajyothi Javeed, Ashir Alawadi, Sadi Alkhabbas, Fahed AlKharabsheh, Khalid Software Engineering Machine Learning Software fault prediction (SFP) is a critical task in software engineering, enabling early identification of faults in modules to improve software quality and reduce maintenance costs. This research investigates the combined effects of feature selection and parameter tuning on the performance of machine learning (ML) models for SFP. This study evaluates the interaction between feature selection methods, including correlation-based feature selection (CFS), recursive feature elimination (RFE), mutual information (MI), and L1 regularization, where hyperparameter tuning techniques such as grid search, randomized search, and genetic algorithm (GA) are used for optimization of ML algorithms, including random forest (RF), logistic regression (LR), and support vector machines (SVM) for optimized fault prediction performance. The combined application of CFS and GA yielded the highest accuracy, achieving 88.40% with RF, representing an improvement of 18% over baseline models without feature selection or tuning. Feature selection reduced dimensionality and identified critical attributes such as weighted methods per Class (WMC) and coupling between objects (CBO), while iterative parameter tuning optimized model alignment to these feature sets. Notably, the proposed methods demonstrated robustness, with minimal cross-validation variability (+-1.0%), and efficiency, reducing training times in univariate methods such as L1 regularization. |
| title | A Feature-Driven Framework for Software Fault Prediction |
| topic | Software Engineering Machine Learning |
| url | https://arxiv.org/abs/2605.17611 |