mc2
OpenML dataset with id 1054
Mike Chapman
Full work available at URL: https://api.openml.org/data/v1/download/53937/mc2.arff
Upload date: 6 October 2014
Dataset Characteristics
Number of classes: 2
Number of features: 40 (numeric: 39, symbolic: 1 and in total binary: 1 )
Number of instances: 161
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.
MC2 Software defect prediction One of the NASA Metrics Data Program defect data sets. The specific type of software is unknown. 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.
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
This page was built for dataset: mc2
