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http://bura.brunel.ac.uk/handle/2438/7926
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DC Field | Value | Language |
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dc.contributor.author | Shepperd, M | - |
dc.contributor.author | Song, Q | - |
dc.contributor.author | Sun, Z | - |
dc.contributor.author | Mair, C | - |
dc.date.accessioned | 2014-01-21T11:44:15Z | - |
dc.date.available | 2014-01-21T11:44:15Z | - |
dc.date.issued | 2013 | - |
dc.identifier.citation | IEEE Transactions on Software Engineering, 39(9), 1208 - 1215, 2013 | en_US |
dc.identifier.issn | 0098-5589 | - |
dc.identifier.uri | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6464273 | en |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/7926 | - |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.subject | Empirical software engineering | en_US |
dc.subject | Data quality | en_US |
dc.subject | Defect prediction | en_US |
dc.subject | Machine learning | en_US |
dc.title | Data quality: Some comments on the NASA software defect datasets | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.1109/TSE.2013.11 | - |
pubs.organisational-data | /Brunel | - |
pubs.organisational-data | /Brunel/Brunel Active Staff | - |
pubs.organisational-data | /Brunel/Brunel Active Staff/School of Info. Systems, Comp & Maths | - |
pubs.organisational-data | /Brunel/Brunel Active Staff/School of Info. Systems, Comp & Maths/IS and Computing | - |
pubs.organisational-data | /Brunel/University Research Centres and Groups | - |
pubs.organisational-data | /Brunel/University Research Centres and Groups/School of Information Systems, Computing and Mathematics - URCs and Groups | - |
pubs.organisational-data | /Brunel/University Research Centres and Groups/School of Information Systems, Computing and Mathematics - URCs and Groups/Centre for Information and Knowledge Management | - |
Appears in Collections: | Computer Science Dept of Computer Science Research Papers |
Files in This Item:
File | Description | Size | Format | |
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TSE_NASADataQualNote_V26.pdf | 165.81 kB | Adobe PDF | View/Open |
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