The Jinx on the NASA software defect data sets

Jean Petrić; David Bowes; Tracy Hall; Bruce Christianson; Nathan Baddoo

doi:10.1145/2915970.2916007

The Jinx on the NASA software defect data sets

Jean Petrić, David Bowes, Tracy Hall, Bruce Christianson, Nathan Baddoo

University of Hertfordshire

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

19 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings of the 20th International Conference on Evaluation and Assessment in Software Engineering (EASE)
Place of Publication	NY, New York
Publisher	ACM Press
Volume	01-03-June-2016
ISBN (Print)	9781450336918
DOIs	https://doi.org/10.1145/2915970.2916007
Publication status	Published - 1 Jun 2016
Event	20th International Conference on Evaluation and Assessment in Software Engineering, EASE 2016 - Limerick, Ireland Duration: 1 Jun 2016 → 3 Jun 2016

Conference

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

Keywords

Data quality
Machine learning
Software defect prediction

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10.1145/2915970.2916007

Cite this

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title = "The Jinx on the NASA software defect data sets",

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.",

keywords = "Data quality, Machine learning, Software defect prediction",

author = "Jean Petri{\'c} and David Bowes and Tracy Hall and Bruce Christianson and Nathan Baddoo",

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doi = "10.1145/2915970.2916007",

language = "English",

isbn = "9781450336918",

volume = "01-03-June-2016",

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Petrić, J, Bowes, D, Hall, T, Christianson, B & Baddoo, N 2016, The Jinx on the NASA software defect data sets. in Proceedings of the 20th International Conference on Evaluation and Assessment in Software Engineering (EASE). vol. 01-03-June-2016, a13, ACM Press, NY, New York , 20th International Conference on Evaluation and Assessment in Software Engineering, EASE 2016, Limerick, Ireland, 1/06/16. https://doi.org/10.1145/2915970.2916007

TY - GEN

T1 - The Jinx on the NASA software defect data sets

AU - Petrić, Jean

AU - Bowes, David

AU - Hall, Tracy

AU - Christianson, Bruce

AU - Baddoo, Nathan

N1 - Jean Petrić, David Bowes, Tracy Hall, Bruce Christianson, Nathan Baddoo, ‘The Jinx on the NASA software defect data sets’ in Proceedings of the 20th International Conference on Evaluation and Assessment in Software Engineering, University of Limerick, Limerick, Ireland, 1-3 June 2016. ISBN 9781450336918. doi: 10.1145/2915970.2916007.

PY - 2016/6/1

Y1 - 2016/6/1

N2 - 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.

AB - 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.

KW - Data quality

KW - Machine learning

KW - Software defect prediction

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M3 - Conference contribution

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SN - 9781450336918

VL - 01-03-June-2016

BT - Proceedings of the 20th International Conference on Evaluation and Assessment in Software Engineering (EASE)

PB - ACM Press

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T2 - 20th International Conference on Evaluation and Assessment in Software Engineering, EASE 2016

Y2 - 1 June 2016 through 3 June 2016

ER -

The Jinx on the NASA software defect data sets

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