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The jinx on the NASA software defect data sets

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Published:01 June 2016Publication History

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.

References

  1. B. Ghotra, S. McIntosh, and A. E. Hassan. Revisiting the impact of classification techniques on the performance of defect prediction models. In 37th Int. Conf. on Software Engineering (ICSE), 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. D. Gray, D. Bowes, N. Davey, Y. Sun, and B. Christianson. The misuse of the NASA metrics data program data sets for automated software defect prediction. In Evaluation Assessment in Software Engineering (EASE 2011), pages 96--103, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  3. D. Gray, D. Bowes, N. Davey, Y. Sun, and B. Christianson. Reflections on the NASA MDP data sets. Software, IET, 6(6):549--558, Dec 2012.Google ScholarGoogle ScholarCross RefCross Ref
  4. T. Hall, S. Beecham, D. Bowes, D. Gray, and S. Counsell. A systematic literature review on fault prediction performance in software engineering. Software Engineering, IEEE Transactions on, 38(6):1276--1304, Nov 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Y. Kamei and E. Shihab. Defect prediction: Accomplishments and future challenges. In Software Analysis, Evolution and Reengineering (SANER), 2016 IEEE 23rd International Conference on, 2016.Google ScholarGoogle Scholar
  6. S. Lessmann, B. Baesens, C. Mues, and S. Pietsch. Benchmarking classification models for software defect prediction: A proposed framework and novel findings. Software Engineering, IEEE Transactions on, 34(4):485--496, July 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. R. Malhotra. A systematic review of machine learning techniques for software fault prediction. Applied Soft Computing, 27:504--518, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. M. Shepperd, Q. Song, Z. Sun, and C. Mair. Data quality: Some comments on the NASA software defect datasets. Software Engineering, IEEE Transactions on, 39(9):1208--1215, Sept 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. R. S. Wahono. A systematic literature review of software defect prediction: Research trends, datasets, methods and frameworks. Journal of Software Engineering, 1(1):1--16, 2015.Google ScholarGoogle Scholar

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  • Published in

    cover image ACM Other conferences
    EASE '16: Proceedings of the 20th International Conference on Evaluation and Assessment in Software Engineering
    June 2016
    310 pages
    ISBN:9781450336918
    DOI:10.1145/2915970

    Copyright © 2016 ACM

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 1 June 2016

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