Randomized trial explores using developer information to enhance fault prediction accuracy in software releases, suggesting novel insights.
Background: Previous research has provided evidence that a combination of static code metrics and software history metrics can be used to predict with surprising success which files in the next release of a large system will have the largest numbers of defects. In contrast, very little research exists to indicate whether information about individual developers can profitably be used to improve predictions.
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Ostrand et al. (2010) studied this question.
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