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. New York, NY, USA: ACM; 2017. p. 214-23. (EASE'17). Abstract
Williams JR, Di Ruscio D, Matragkas N, Di Rocco J, Kolovos DS. Models of OSS Project Meta-information: A Dataset of Three Forges. In: Proceedings of the 11th Working Conference on Mining Software Repositories. New York, NY, USA: ACM; 2014. p. 408-11. (MSR 2014). Abstract  Download: Models_of_OSS_Project_Meta-Information_A_Dataset_of_Three_Forges_draft.pdf (334.67 KB)
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. New York, NY, USA: ACM; 2014. p. 356-9. (MSR 2014). Abstract
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. Abstract  Download: 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. New York, NY, USA: ACM; 2006. p. 119-25. (MSR '06). Abstract  Download: 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. New York, NY, USA: ACM; 2005. p. 74-8. (MSR '05). Abstract  Download: 74LinearPredictive.pdf (190.25 KB)