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
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
Proceedings of the 28th international conference on Software engineering
An open framework for CVS repository querying, analysis and visualization
Proceedings of the 2006 international workshop on Mining software repositories
Mining sequences of changed-files from version histories
Proceedings of the 2006 international workshop on Mining software repositories
Fine-grained processing of CVS archives with APFEL
eclipse '06 Proceedings of the 2006 OOPSLA workshop on eclipse technology eXchange
Visual assessment of software evolution
Science of Computer Programming
Visual Analytics: Visual data mining and analysis of software repositories
Computers and Graphics
Journal of Software Maintenance and Evolution: Research and Practice
Visual querying and analysis of large software repositories
Empirical Software Engineering
Analyzing a Software Process Model Repository for Understanding Model Evolution
ICSP '09 Proceedings of the International Conference on Software Process: Trustworthy Software Development Processes
Measuring behavioral dependency for improving change-proneness prediction in UML-based design models
Journal of Systems and Software
The ability of object-oriented metrics to predict change-proneness: a meta-analysis
Empirical Software Engineering
Comparison and evaluation of source code mining tools and techniques: A qualitative approach
Intelligent Data Analysis
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During software evolution, adaptive, and corrective maintenance are common reasons for changes. Often such changes cluster around key components. It is therefore important to analyze the frequency of changes to individual classes, but, more importantly, to also identify and show related changes in multiple classes. Frequent changes in clusters of classes may be due to their importance, due to the underlying architecture or due to chronic problems. Knowing where those change-prone clusters are can help focus attention, identify targets for re-engineering and thus provide product-based information to steer maintenance processes. This paper describes a method to identify and visualize classes and class interactions that are the most change-prone. The method was applied to a commercial embedded, real-time software system. It is object-oriented software that was developed using design patterns.