Resources

Just R, Jalali D, Ernst MD (2014) Defects4j: A database of existing faults to enable controlled testing studies for java programs. In: Proc. of the 2014 Int. Symposium on Softw. Testing and Analysis (ISSTA), ACM repository

Gyimesi P, Vancsics B, Stocco A, Mazinanian D, Beszedes A, Ferenc R, Mesbah A (2019) Bugsjs: a benchmark of javascript bugs. In: 2019 12th IEEE Conference on Software Testing, Validation and Veriﬁcation (ICST), pp 90101

Le Goues C, Holtschulte N, Smith EK, Brun Y, Devanbu P, Forrest S, Weimer W (2015) The manybugs and introclass benchmarks for automated repair of c programs. IEEE Transactions on Software Engineering 41(12):1236–1256

Saha R, Lyu Y, Lam W, Yoshida H, Prasad M (2018) Bugs.jar: A largescale, diverse dataset of real-world java bugs. In: 2018 IEEE/ACM 15th International Conference on Mining Software Repositories (MSR), pp 10–13

Herbold S, Trautsch A, Trautsch F, Ledel B (2019) Issues with szz: An empirical assessment of the state of practice of defect prediction data collection. 1911.08938

https://github.com/rpau/git-commit-classifier

Enabling the Continuous Analysis of Security Vulnerabilities with VulData7 repository

Mills C, Parra E, Pantiuchina J, Bavota G, Haiduc S (2020) On the relationship between bug reports and queries for text retrieval-based bug localization. Empirical Software Engineering pp 1–42

Wang Q, Parnin C, Orso A (2015) Evaluating the usefulness of ir-based fault localization techniques. In: Proceedings of the 2015 International Symposium on Software Testing and Analysis, Association for Computing Machinery, New York, NY, USA, ISSTA 2015, p 1–11, DOI 10.1145/2771783.2771797, URL https://doi.org/10.1145/2771783.2771797

Ye X, Bunescu R, Liu C (2015) Mapping bug reports to relevant ﬁles: A ranking model, a ﬁne-grained benchmark, and feature evaluation. IEEE Transactions on Software Engineering 42(4):379–402

Chaparro O, Marcus A (2016) On the reduction of verbose queries in text retrieval based software maintenance. In: 2016 IEEE/ACM 38th International Conference on Software Engineering Companion (ICSE-C), IEEE, pp 716–718

S. Levin and A. Yehudai. Boosting automatic commit classification into maintenance activities by utilizing source code changes. In Proceedings of the 13th International Conference on Predictive Models and Data Analytics in Software Engineering, PROMISE, pages 97–106, New York, NY, USA, 2017. ACM

B. Ray, D. Posnett, V. Filkov, and P. Devanbu. A large scale study of programming languages and code quality in github. In Proceedings of the 22Nd ACM SIGSOFT International Symposium on Foundations of Software Engineering, FSE 2014, pages 155–165, New York, NY, USA, 2014. ACM.

N. C. Shrikanth and T. Menzies. Assessing practitioner beliefs about software defect prediction. In Intl. Conf. Softw. Eng., number 42, May 2020.

A. Hindle, D. M. German, M. W. Godfrey, and R. C. Holt. Automatic classication of large changes into maintenance categories. In 2009 IEEE 17th International Conference on Program Comprehension, pages 30–39, May 2009

J. J. Amor, G. Robles, J. M. Gonzalez-Barahona, and A. Navarro. Discriminating development activities in versioning systems: A case study, Jan 2006.

G. Antoniol, K. Ayari, M. Di Penta, F. Khomh, and Y.-G. Guéhéneuc. Is it a bug or an enhancement? a text-based approach to classify change requests. In Proceedings of the 2008 Conference of the Center for Advanced Studies on Collaborative Research: Meeting of Minds, CASCON ’08, New York, NY, USA, 2008. Association for Computing Machinery