Contains the keyword data mining

Izquierdo-Cortazar D, Sekitoleko N, Gonzalez-Barahona JM, Kurth L. Using Metrics to Track Code Review Performance. In Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering [Internet]. New York, NY, USA: ACM; 2017. pp. 214–223. http://doi.acm.org/10.1145/3084226.3084247
Matragkas N, Williams JR, Kolovos DS, Paige RF. Analysing the 'Biodiversity' of Open Source Ecosystems: The GitHub Case. In Proceedings of the 11th Working Conference on Mining Software Repositories [Internet]. New York, NY, USA: ACM; 2014. pp. 356–359. http://doi.acm.org/10.1145/2597073.2597119
Squire M, Funkhouser C. "A bit of code": How the Stack Overflow Community Creates Quality Postings. 47th International Hawai'i Conference on System Sciences (HICSS-47). IEEE Computer Society; 2014. pp. 1425-1434. PDF icon hicssSMFinalWatermark.pdf (867 KB)
Bird C, Bachman A, Rahman F, Bernstein A. Linkster: Enabling Efficient Manual Mining. In Demonstration Track, Proceedings of the 17th SIGSOFT Symposium on Foundations of Software Engineering. ACM; 2010. PDF icon bird2010lee.pdf (627.48 KB)
Knab P, Pinzger M, Bernstein A. Predicting defect densities in source code files with decision tree learners. In Proceedings of the 2006 international workshop on Mining software repositories [Internet]. New York, NY, USA: ACM; 2006. pp. 119–125. http://doi.acm.org/10.1145/1137983.1138012PDF icon 119Predicting.pdf (671.54 KB)
Antoniol G, Rollo VF, Venturi G. Linear predictive coding and cepstrum coefficients for mining time variant information from software repositories. In Proceedings of the 2005 international workshop on Mining software repositories [Internet]. New York, NY, USA: ACM; 2005. pp. 74-78. http://doi.acm.org/10.1145/1082983.1083156PDF icon 74LinearPredictive.pdf (190.25 KB)