Predicting the Location and Number of Faults in Large Software Systems
IEEE Transactions on Software Engineering
Empirical Software Engineering
Does calling structure information improve the accuracy of fault prediction?
MSR '09 Proceedings of the 2009 6th IEEE International Working Conference on Mining Software Repositories
Comparing the effectiveness of several modeling methods for fault prediction
Empirical Software Engineering
On the improvement of a fault classification scheme with implications for white-box testing
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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We have developed an interactive tool that predicts fault likelihood for the individual files of successive releases of large, long-lived, multi-developer software systems. Predictions are the result of a two-stage process: first, the extraction of current and historical properties of the system, and second, application of a negative binomial regression model to the extracted data. The prediction model is presented to the user as a GUI-based tool that requires minimal input from the user, and delivers its output as an ordered list of the system's files together with an expected percent of faults each file will have in the release about to undergo system test. The predictions can be used to prioritize testing efforts, to plan code or design reviews, to allocate human and computer resources, and to decide if files should be rewritten.