Identifying fault-prone files in large industrial software systems
E. Weyuker; T. Ostrand Mälerdalen University, Västerås, Sweden
Abstract
We provide an overview of a decade-long research program aimed at identifying the most fault-prone files of large industrial software systems. We describe the motivation, approach used and results observed when we applied this technology to 170 releases of 9 different systems running continuously in the field. In all cases the files predicted to be most fault-prone accounted for most of the bugs in the system.
Keywords
Software fault prediction; Fault-proneness; Predictive model; Standard Model; Industrial software
Acknowledgment
This work was supported in part by the Swedish Research Council through ...
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