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Assessing the Impact of Untraceable Bugs on the Quality of Software Defect Prediction Datasets

The results of empirical case studies in Software Defect Prediction are dependent on data obtained by mining and linking separate software repositories. These data often suffer from low quality. In order to overcome this problem, we have already investigated all the issues that influence the data collection process, proposed a systematic data collection procedure and evaluated it. The proposed collection procedure is implemented in the Bug-Code Analyzer tool and used on several projects from the Eclipse open source community. In this paper, we perform additional analysis of the collected data quality. We investigate the impact of untraceable bugs on non- fault-prone category of files, which is, to the best of our knowledge, an issue that has never been addressed. Our results reveal this issue should not be an underestimated one and should be reported along with bugs’ linking rate as a measure of dataset quality.

data quality; untraceable bugs; fault-proneness

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