pc1

URL

Notes on the Data Set

This is a defect data set that contains Halstead and McCabe metrics:

  • A tutorial on defect prediction can be found here.
  • Information about Halstead metrics can be found here.
  • Information about McCabe metrics can be found here.

Change Log

When What
February 2, 2016 Fixing dead paper link, Adding BibTeX
Nov 2011 Dataset has been cleaned by MartinShepperd et al. (pc1’ and pc1’’ are the cleaned versions)
December 2, 2004 Donated by Tim Menzies

This is one of the NASA Metrics Data Program defect data sets.

Reference

“How Good is Your Blind Spot Sampling Policy?” Available from http://menzies.us/pdf/03blind.pdf.

@INPROCEEDINGS{1281737,
author={Menzies, T. and Di Stefano, J.S.},
booktitle={High Assurance Systems Engineering, 2004. Proceedings. Eighth IEEE International Symposium on},
title={How good is your blind spot sampling policy},
year={2004},
pages={129-138},
keywords={formal specification;program verification;sampling methods;software metrics;automatic formal methods;black box probing;blind spot sampling;
            defect detectors;formal specification;public domain defect data;software assessment;Aerospace engineering;Computer science;Costs;
            Detectors;Mission critical systems;NASA;Project management;Proposals;Sampling methods;Systems engineering and theory},
doi={10.1109/HASE.2004.1281737},
ISSN={1530-2059},
month={March},}



@ARTICLE{6464273,
author={Shepperd, M. and Qinbao Song and Zhongbin Sun and Mair, C.},
journal={Software Engineering, IEEE Transactions on},
title={Data Quality: Some Comments on the NASA Software Defect Datasets},
year={2013},
volume={39},
number={9},
pages={1208-1215},
keywords={data analysis;learning (artificial intelligence);pattern classification;software reliability;IEEE Transactions on Software Engineering;
          NASA software defect dataset;National Aeronautics and Space Administration;data preprocessing;data quality;data replication;
          dataset provenance;defect-prone classification;machine learning;not-defect-prone classification;
          software module classification;Abstracts;Communities;Educational institutions;NASA;PROM;Software;Sun;Empirical software engineering;
          data quality;defect prediction;machine learning},
doi={10.1109/TSE.2013.11},
ISSN={0098-5589},
month={Sept},}