Predicting Fault Incidence Using Software Change History
IEEE Transactions on Software Engineering
MSR 2004: International Workshop on Mining Software Repositories
Proceedings of the 26th International Conference on Software Engineering
Enriching revision history with interactions
Proceedings of the 2006 international workshop on Mining software repositories
Correctness of data mined from CVS
Proceedings of the 2008 international working conference on Mining software repositories
Retina: helping students and instructors based on observed programming activities
Proceedings of the 40th ACM technical symposium on Computer science education
Estimating programming knowledge with Bayesian knowledge tracing
ITiCSE '09 Proceedings of the 14th annual ACM SIGCSE conference on Innovation and technology in computer science education
Comparing effective and ineffective behaviors of student programmers
ICER '09 Proceedings of the fifth international workshop on Computing education research workshop
ICSP'08 Proceedings of the Software process, 2008 international conference on Making globally distributed software development a success story
Using the SCORE software package to analyse novice computer graphics programming
Proceedings of the 16th annual joint conference on Innovation and technology in computer science education
Creating Process-Agents incrementally by mining process asset library
Information Sciences: an International Journal
The MSR cookbook: mining a decade of research
Proceedings of the 10th Working Conference on Mining Software Repositories
Recording and analyzing in-browser programming sessions
Proceedings of the 13th Koli Calling International Conference on Computing Education Research
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Over 200 CVS repositories representing the assignments of students in a second year undergraduate computer science course have been assembled. This unique data set represents many individuals working separately on identical projects, presenting the opportunity to evaluate the effects of the work habits captured by CVS on performance. This paper outlines our experiences mining and analyzing these repositories. We extracted various quantitative measures of student behaviour and code quality, and attempted to correlate these features with grades. Despite examining 166 features, we find that grade performance cannot be accurately predicted; certainly no predictors stronger than simple lines-of-code were found.