Abstract The ability to predict the time required to repair software defects is important for both software quality management and maintenance. Estimated repair times can be used to improve the reliability and time-to-market of software under development. This paper presents an empirical approach to predicting defect repair times by constructing models that use well-established machine learning algorithms and defect data from past software defect reports. We describe, as a case study, the analysis of defect reports collected during the development of a large medical software system. Our predictive models give accuracies as high as 93.44%, despite the limitations of the available data. We present the proposed methodology along with detailed experimental results, which include comparisons with other analytical modeling approaches.
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