Thomas J. Ostrand
Elaine J. Weyuker
ABSTRACT
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.
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Cross Ref
- T. J. Ostrand, E. J. Weyuker, and R. M. Bell. Predicting the Location and Number of Faults in Large Software Systems. IEEE Trans. on Software Engineering, Vol 31, No 4, April 2005. Google ScholarDigital Library
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- E. J. Weyuker, T. J. Ostrand, and R. M. Bell. Do Too Many Cooks Spoil the Broth? Using the Number of Developers to Enhance Defect Prediction Models, Empirical Software Eng., Vol 13, No. 5, October 2008. Google ScholarDigital Library
- E. J. Weyuker, T. J. Ostrand, and R. M. Bell. Comparing the Effectiveness of Several Modeling Methods for Fault Prediction, Empirical Software Eng., June 2009. Google ScholarDigital Library
- Y. Shin, R. M. Bell, T. J. Ostrand, E. J. Weyuker. Does Calling Structure Information Improve the Accuracy of Fault Prediction?, Proc. Mining Software Repositories, Vancouver, May 2009. Google ScholarDigital Library
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Index Terms
- Software fault prediction tool
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