General Aim

Software Defect Prediction (SDP) seeks to identify defective components before testing, thereby improving the efficiency of software quality assurance processes. This article discusses potential future research directions and the challenges that may arise.

Defect Prediction Process

• Data Collection: Code repositories, version control systems, and issue tracking tools.
• Data Preprocessing: Cleaning, feature selection, normalization, and handling class imbalance.
• Model Construction: Machine learning algorithms and deep learning approaches.
• Prediction: Identifying potentially defective components in advance.
• Evaluation: Metrics such as AUC, MCC, F1, and effort-aware measures.
• Interpretation: Explaining model decisions to build trust and transparency.

Future Research Directions

1. Data Quality: More accurate labeling (particularly improvements to the SZZ algorithm).
2. Data Sharing: Privacy-preserving approaches (e.g., federated learning).
3. Metric Fusion: Integration of code metrics, process metrics, and semantic representations.
4. Line-Level Defect Prediction: Moving from file- or class-level prediction to pinpointing defective lines of code.
5. Robust and Stable Models: Hyperparameter optimization and improved stability.
6. Unsupervised Prediction: Methods that operate without labeled data.
7. Effort-Awareness: Considering actual inspection and testing effort, rather than proxies such as LOC.
8. Explainability and Actionability: Ensuring predictions are understandable and practically useful.

Conclusion

Future research should aim to make SDP not only accurate in prediction but also explainable, trustworthy, and practically applicable in real-world software engineering contexts.