Automatic clustering of bug reports

It is widely accepted that most development cost is spent for maintenance and most of the maintenance cost is spent on comprehension. Maintainers need to understand the current status of the code before updating it. For this reason, they examine pervious change requests and previous code changes to understand how the current code was evolved. The problem that faces them is how to locate related previous change requests that handled a specific feature or topic in the code. Quickly locating previous related change requests help developers to quickly understand the current status of the code and hence reduce the maintenance cost which is our ultimate goal. This paper proposes an automated technique to identify related previous change requests stored in bug reports. The technique is based on clustering bug reports based on their textual similarities. The result of the clustering is disjoint clusters of related bug reports that have common issues, topic or feature. A set of terms is extracted from each cluster, as tags, to help maintainers to understand the issue, topic or feature handled by the bug reports in the cluster. An experimental study is applied and discussed, followed by manual evaluation of the bug reports in the generated clusters.