kc2
OpenML dataset with id 1063
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Full work available at URL: https://api.openml.org/data/v1/download/53946/kc2.arff
Upload date: 6 October 2014
Dataset Characteristics
Number of classes: 2
Number of features: 22 (numeric: 21, symbolic: 1 and in total binary: 1 )
Number of instances: 522
Number of instances with missing values: 0
Number of missing values: 0
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.
KC2 Software defect prediction One of the NASA Metrics Data Program defect data sets. Data from software for science data processing. 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
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