Large Defect Prediction Benchmark

This is a collection of defect datasets for the software engineering research community. This collection is from 8 corpus as follows:

1. AEEEM-defect-dataset: M. D’Ambros, M. Lanza, and R. Robbes, “Evaluating defect prediction approaches: A benchmark and an extensive comparison,”Empirical Softw. Engg., vol. 17, no. 4-5, pp. 531–577, Aug. 2012.
2. JIRA-defect-dataset: S. Yatish, J. Jiarpakdee, P. Thongtanunam, and C. Tantithamthavorn,“Mining software defects: Should we consider affected releases?” in The International Conference on Software Engineering (ICSE), 2019.
3. JIT-defect-datasets S. McIntosh, Y. Kamei, "Are Fix-Inducing Changes a Moving Target? A Longitudinal Case Study of Just-In-Time Defect Prediction", IEEE Transactions on Software Engineering (TSE).
4. Line-level-defect-prediction: S Wattanakriengkrai, P Thongtanunam, C Tantithamthavorn, H Hata, and K Matsumoto, "Predicting Defective Lines Using a Model-Agnostic Technique", IEEE Transactions on Software Engineering (TSE).
5. NASA-defect-dataset: 
6. Relink-defect-dataset: R. Wu, H Zhang, S Kim, and SC. Cheung. "Relink: recovering links between bugs and changes." In Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering, pp. 15-25. 2011.

7. TeraPromise-defect-dataset: M. Jureczko and L. Madeyski, “Towards identifying software project clusters with regard to defect prediction,” in Proceedings of the 6th International Conference on Predictive Models in Software Engineering, ser. PROMISE ’10. New York, NY, USA: ACM, 2010, pp. 9:1–9:10.

8. Zimmermann-defect-dataset  T. Zimmermann, R Premraj, A Zeller. "Predicting defects for Eclipse." Third International Workshop on Predictor Models in Software Engineering (PROMISE'07: ICSE Workshops 2007). IEEE, 2007.