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    Rights statement: © 2016 ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in EASE '16 Proceedings of the 20th International Conference on Evaluation and Assessment in Software Engineering http://dx.doi.org/10.1145/2915970.2916007

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The Jinx on the NASA software defect data sets

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Publication date1/06/2016
Host publicationEASE '16 Proceedings of the 20th International Conference on Evaluation and Assessment in Software Engineering
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Number of pages5
ISBN (electronic)9781450336918
<mark>Original language</mark>English
Event20th International Conference on Evaluation and Assessment in Software Engineering, EASE 2016 - Limerick, Ireland
Duration: 1/06/20163/06/2016

Conference

Conference20th International Conference on Evaluation and Assessment in Software Engineering, EASE 2016
Country/TerritoryIreland
CityLimerick
Period1/06/163/06/16

Conference

Conference20th International Conference on Evaluation and Assessment in Software Engineering, EASE 2016
Country/TerritoryIreland
CityLimerick
Period1/06/163/06/16

Abstract

Background: The NASA datasets have previously been used extensively in studies of software defects. In 2013 Shepperd et al. presented an essential set of rules for removing erroneous data from the NASA datasets making this data more reliable to use. Objective: We have now found additional rules necessary for removing problematic data which were not identified by Shepperd et al. Results: In this paper, we demonstrate the level of erroneous data still present even after cleaning using Shepperd et al.'s rules and apply our new rules to remove this erroneous data. Conclusion: Even after systematic data cleaning of the NASA MDP datasets, we found new erroneous data. Data quality should always be explicitly considered by researchers before use.

Bibliographic note

© 2016 ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in EASE '16 Proceedings of the 20th International Conference on Evaluation and Assessment in Software Engineering http://dx.doi.org/10.1145/2915970.2916007