CVSscan: visualization of code evolution
SoftVis '05 Proceedings of the 2005 ACM symposium on Software visualization
Toward Understanding the Rhetoric of Small Source Code Changes
IEEE Transactions on Software Engineering
Generating overview summaries of ongoing email thread discussions
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Summarizing email conversations with clue words
Proceedings of the 16th international conference on World Wide Web
ACM Transactions on Software Engineering and Methodology (TOSEM)
Change Distilling: Tree Differencing for Fine-Grained Source Code Change Extraction
IEEE Transactions on Software Engineering
Multi-document summarization using cluster-based link analysis
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Summarizing software artifacts: a case study of bug reports
Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1
Towards automatically generating summary comments for Java methods
Proceedings of the IEEE/ACM international conference on Automated software engineering
On the Use of Automated Text Summarization Techniques for Summarizing Source Code
WCRE '10 Proceedings of the 2010 17th Working Conference on Reverse Engineering
Generating natural language summaries for crosscutting source code concerns
ICSM '11 Proceedings of the 2011 27th IEEE International Conference on Software Maintenance
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When a developer works on code that is shared with other developers, she needs to know why the code has been changed in particular ways to avoid reintroducing bugs. A developer looking at a code change may have access to a short commit message or a link to a bug report which may provide detailed information about how the code changed but which often lacks information about what motivated the change. This motivational information can sometimes be found by piecing together information from a set of relevant project documents, but few developers have the time to find and read the right documentation. We propose the use of multi-document summarization techniques to generate a concise natural language description of why code changed so that a developer can choose the right course of action.