Software Fault Prediction through Hybrid Learning Algorithms with Integrated Change Metrics

Software fault prediction (SFP) is essential for improving software reliability and reducing development costs by identifying fault-prone modules early. This paper investigates the integration of change metrics—such as code churn, commit frequency, and modification history—with hybrid learning algorithms to enhance prediction accuracy. We propose a novel hybrid framework that combines genetic algorithms (GA) for feature selection, convolutional neural networks (CNN) for extracting semantic and temporal features from change data, and multi-layer perceptrons (MLP) for processing traditional static metrics. These components are fused using a gated attention mechanism to optimize predictions. Empirical evaluations on datasets from the PROMISE repository, including projects like Ant, Camel, and Xerces, demonstrate that our approach achieves superior performance in terms of F1-score (0.87), AUC (0.90), and effort-aware metrics like PofB20 (0.55), outperforming existing hybrid models by 8-15%. The study addresses key challenges such as class imbalance, cross-project generalizability, and the underutilization of dynamic change data. By incorporating real-time evolutionary metrics, our model enables more proactive defect management, contributing to advancements in software engineering practices.

Software Fault Prediction, Hybrid Learning, Change Metrics, Genetic Algorithms, Deep Learning

1. Rhmann, W., et al. (2019). Software fault prediction based on change metrics using hybrid algorithms: An empirical study. Journal of King Saud University - Computer and Information Sciences. [Google Scholar] [Crossref]

2. Khan, S. A., et al. (2023). An automated software failure prediction technique using hybrid machine learning algorithms. Measurement: Sensors. [Google Scholar] [Crossref]

3. Alsaeedi, A., & Khan, M. Z. (2019). Software Defect Prediction Using Supervised Machine Learning and Ensemble Techniques: A Comparative Study. Journal of Software Engineering and Applications. [Google Scholar] [Crossref]

4. Alenezi, M., & Zarour, M. (2024). Semantic and traditional feature fusion for software defect prediction using hybrid deep learning model. Scientific Reports. [Google Scholar] [Crossref]

5. Koya, J. R., et al. (2024). Software Defect Prediction Using Hybrid Machine Learning Techniques: A Comparative Study. SN Computer Science. [Google Scholar] [Crossref]

6. Nam, J., et al. (2013). Heterogeneous defect prediction. IEEE Transactions on Software Engineering. [Google Scholar] [Crossref]

7. Hassan, A. E. (2009). Predicting faults using the complexity of code changes. ICSE. [Google Scholar] [Crossref]

8. Zimmermann, T., et al. (2009). Cross-project defect prediction: a large scale experiment on data vs. domain vs. process. ESEC/FSE. [Google Scholar] [Crossref]

9. Lessmann, S., et al. (2008). Benchmarking classification models for software defect prediction: A proposed framework and novel findings. IEEE TSE. [Google Scholar] [Crossref]

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