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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Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Publication date | 1/06/2016 |
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Host publication | EASE '16 Proceedings of the 20th International Conference on Evaluation and Assessment in Software Engineering |
Place of Publication | New York |
Publisher | Association for Computing Machinery, Inc |
Number of pages | 5 |
ISBN (electronic) | 9781450336918 |
<mark>Original language</mark> | English |
Event | 20th International Conference on Evaluation and Assessment in Software Engineering, EASE 2016 - Limerick, Ireland Duration: 1/06/2016 → 3/06/2016 |
Conference | 20th International Conference on Evaluation and Assessment in Software Engineering, EASE 2016 |
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Country/Territory | Ireland |
City | Limerick |
Period | 1/06/16 → 3/06/16 |
Conference | 20th International Conference on Evaluation and Assessment in Software Engineering, EASE 2016 |
---|---|
Country/Territory | Ireland |
City | Limerick |
Period | 1/06/16 → 3/06/16 |
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.