Details

Autor(en) / Beteiligte

• Shepperd, M.
• Qinbao Song
• Zhongbin Sun
• Mair, C.

Titel

Data Quality: Some Comments on the NASA Software Defect Datasets

Ist Teil von

• IEEE transactions on software engineering, 2013-09, Vol.39 (9), p.1208-1215

Ort / Verlag

New York: IEEE

Erscheinungsjahr

2013

Link zum Volltext

• Volltext

Quelle

IEEE Xplore

Beschreibungen/Notizen

• 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.