Mining Version Histories to Guide Software Changes
Proceedings of the 26th International Conference on Software Engineering
Mining Version Histories to Guide Software Changes
IEEE Transactions on Software Engineering
Correlating Social Interactions to Release History during Software Evolution
MSR '07 Proceedings of the Fourth International Workshop on Mining Software Repositories
Using concept analysis to detect co-change patterns
Ninth international workshop on Principles of software evolution: in conjunction with the 6th ESEC/FSE joint meeting
Journal of Software Maintenance and Evolution: Research and Practice
Using information scent to model the dynamic foraging behavior of programmers in maintenance tasks
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Adaptive Detection of Design Flaws
Electronic Notes in Theoretical Computer Science (ENTCS)
EQ-mine: predicting short-term defects for software evolution
FASE'07 Proceedings of the 10th international conference on Fundamental approaches to software engineering
Clustering source code files to predict change propagation during software maintenance
Proceedings of the 50th Annual Southeast Regional Conference
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A considerable amount of system maintenanceexperience can be found in bug tracking and source codeconfiguration management systems. Data mining andmachine learning techniques allow one to extract modelsfrom past experience that can be used in futurepredictions. By mining the software change record, onecan therefore generate models that can be used in futuremaintenance activities. In this paper we present anexample of such a model that represents a relationbetween pairs of files and show how it can be extractedfrom the software update records of a real world legacysystem. We show how different sources of data can beused to extract sets of features useful in describing thismodel, as well as how results are affected by thesedifferent feature sets and their combinations. Our bestresults were obtained from text-based features, i.e. thoseextracted from words in the problem reports as opposedto syntactic structures in the source code.