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Predicting source code changes by mining revision history Ying, Annie Tsui Tsui

Abstract

Software developers are often faced with modification tasks that involve source which is spread across a code base. Some dependencies between source, such as the dependencies between platform dependent fragments, cannot be determined by existing static and dynamic analyses. To help developers identify relevant source code during a modification task, we have developed an approach that applies data mining techniques to determine change patterns—files that were changed together frequently in the past—from the revision history of the code base. Our hypothesis is that the change patterns can be used to recommend potentially relevant source code to a developer performing a modification task. We show that this approach can reveal valuable dependencies by applying the approach to the Eclipse and Mozilla open source projects, and by evaluating the predictability and interestingness of the recommendations produced.

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