Predicting the Location and Number of Faults in Large Software Systems
IEEE Transactions on Software Engineering
Looking for bugs in all the right places
Proceedings of the 2006 international symposium on Software testing and analysis
Automating algorithms for the identification of fault-prone files
Proceedings of the 2007 international symposium on Software testing and analysis
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We have designed and implemented a tool that predicts files most likely to have defects in a future release of a large software system. The tool builds a regression model based on the version and defect history of the system, and produces a list of the next release's most probable fault-prone files, sorted in decreasing order of the number of predicted defects. Testers can use this information to decide where to focus resources, and to help determine how much effort to allocate to various parts of the system. Developers can use the tool's output to help decide whether files should be rewritten rather than patched. A prototype version of the tool has been integrated with AT&T's internal software change management system, providing seamless access to the system's version and defect information, and giving users a simple interface to the tool's output.