Applying Semantic Techniques to Search and Analyze Bug Tracking Data

The Web has become an important knowledge source for resolving system installation problems and for working around software bugs. In particular, web-based bug tracking systems offer large archives of useful troubleshooting advice. However, searching bug tracking systems can be time consuming since generic search engines do not take advantage of the semi-structured knowledge recorded in bug tracking systems. We present work towards a semantics-based bug search system which tries to take advantage of the semi-structured data found in many widely used bug tracking systems. We present a study of bug tracking systems and we describe how to crawl them in order to extract semi-structured data. We describe a unified data model to store bug tracking data. The model has been derived from the analysis of the most popular systems. Finally, we describe how the crawled data can be fed into a semantic search engine to facilitate semantic search.