Identifying objects using cluster and concept analysis
Proceedings of the 21st international conference on Software engineering
ACM Computing Surveys (CSUR)
DynaMine: finding common error patterns by mining software revision histories
Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering
Classifying Change Types for Qualifying Change Couplings
ICPC '06 Proceedings of the 14th IEEE International Conference on Program Comprehension
Mining Aspects from Version History
ASE '06 Proceedings of the 21st IEEE/ACM International Conference on Automated Software Engineering
Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering
Automatic Inference of Structural Changes for Matching across Program Versions
ICSE '07 Proceedings of the 29th international conference on Software Engineering
Change Distilling: Tree Differencing for Fine-Grained Source Code Change Extraction
IEEE Transactions on Software Engineering
Hierarchical Clustering for Software Architecture Recovery
IEEE Transactions on Software Engineering
Empirical Evaluation of Strategies to Detect Logical Change Dependencies
SOFSEM '10 Proceedings of the 36th Conference on Current Trends in Theory and Practice of Computer Science
Capturing the long-term impact of changes
Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 2
Comparing fine-grained source code changes and code churn for bug prediction
Proceedings of the 8th Working Conference on Mining Software Repositories
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The reasons why software is changed are manyfold; new features are added, bugs have to be fixed, or the consistency of coding rules has to be re-established. Since there are many types of of source code changes we want to explore whether they appear frequently together in time and whether they describe specific development activities. We describe a semi-automated approach to discover patterns of such change types using agglomerative hierarchical clustering. We extracted source code changes of one commercial and two open-source software systems and applied the clustering. We found that change type patterns do describe development activities and affect the control flow, the exception flow, or change the API.