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README.md
README.md
 
 
datapackage.json
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features.json
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kc1.csv
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README.md

The resources for this dataset can be found at https://www.openml.org/d/1067

Author: Mike Chapman, NASA
Source: tera-PROMISE - 2004
Please cite: Sayyad Shirabad, J. and Menzies, T.J. (2005) The PROMISE Repository of Software Engineering Databases. School of Information Technology and Engineering, University of Ottawa, Canada.

KC1 Software defect prediction
One of the NASA Metrics Data Program defect data sets. Data from software for storage management for receiving and processing ground data. Data comes from McCabe and Halstead features extractors of source code. These features were defined in the 70s in an attempt to objectively characterize code features that are associated with software quality.

Attribute Information

  1. loc : numeric % McCabe's line count of code
  2. v(g) : numeric % McCabe "cyclomatic complexity"
  3. ev(g) : numeric % McCabe "essential complexity"
  4. iv(g) : numeric % McCabe "design complexity"
  5. n : numeric % Halstead total operators + operands
  6. v : numeric % Halstead "volume"
  7. l : numeric % Halstead "program length"
  8. d : numeric % Halstead "difficulty"
  9. i : numeric % Halstead "intelligence"
  10. e : numeric % Halstead "effort"
  11. b : numeric % Halstead
  12. t : numeric % Halstead's time estimator
  13. lOCode : numeric % Halstead's line count
  14. lOComment : numeric % Halstead's count of lines of comments
  15. lOBlank : numeric % Halstead's count of blank lines
  16. lOCodeAndComment: numeric
  17. uniq_Op : numeric % unique operators
  18. uniq_Opnd : numeric % unique operands
  19. total_Op : numeric % total operators
  20. total_Opnd : numeric % total operands
  21. branchCount : numeric % of the flow graph
  22. problems : {false,true} % module has/has not one or more reported defects

Relevant papers

  • Shepperd, M. and Qinbao Song and Zhongbin Sun and Mair, C. (2013) Data Quality: Some Comments on the NASA Software Defect Datasets, IEEE Transactions on Software Engineering, 39.

  • Tim Menzies and Justin S. Di Stefano (2004) How Good is Your Blind Spot Sampling Policy? 2004 IEEE Conference on High Assurance Software Engineering.

  • T. Menzies and J. DiStefano and A. Orrego and R. Chapman (2004) Assessing Predictors of Software Defects", Workshop on Predictive Software Models, Chicago