Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/7926
Title: Data quality: Some comments on the NASA software defect datasets
Authors: Shepperd, M
Song, Q
Sun, Z
Mair, C
Keywords: Empirical software engineering;Data quality;Defect prediction;Machine learning
Issue Date: 2013
Publisher: Institute of Electrical and Electronics Engineers
Citation: IEEE Transactions on Software Engineering, 39(9), 1208 - 1215, 2013
Abstract: 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.
URI: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6464273
http://bura.brunel.ac.uk/handle/2438/7926
DOI: http://dx.doi.org/10.1109/TSE.2013.11
ISSN: 0098-5589
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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