Data Quality: Some Comments on the NASA Software Defect Datasets

作者：Shepperd, Martin^*; Song, Qinbao; Sun, Zhongbin; Mair, Carolyn

来源：IEEE Transactions on Software Engineering, 2013, 39(9): 1208-1215.

DOI：10.1109/TSE.2013.11

• Empirical software engineering
• data quality
• machine learning
• defect prediction

Background-Self-evidently empirical analyses rely upon the quality of their data. Likewise, replications rely upon accurate reporting and using the same rather than similar versions of datasets. In recent years, there has been much interest in using machine learners to classify software modules into defect-prone and not defect-prone categories. The publicly available NASA datasets have been extensively used as part of this research. Objective-This short note investigates the extent to which published analyses based on the NASA defect datasets are meaningful and comparable. Method-We analyze the five studies published in the IEEE Transactions on Software Engineering since 2007 that have utilized these datasets and compare the two versions of the datasets currently in use. Results-We find important differences between the two versions of the datasets, implausible values in one dataset and generally insufficient detail documented on dataset preprocessing. Conclusions-It is recommended that researchers 1) indicate the provenance of the datasets they use, 2) report any preprocessing in sufficient detail to enable meaningful replication, and 3) invest effort in understanding the data prior to applying machine learners.

• 出版日期2013-9
• 单位西安交通大学