An initial study of the growth of eclipse defects

ABSTRACT

We analyze the Eclipse defect data from June 2004 to November 2007, and find that the growth of the number of defects can be well modeled by polynomial functions. Furthermore, we can predict the number of future Eclipse defects based on the nature of defect growth.

REFERENCES

• 1: Carina Andersson , Per Runeson, A Replicated Quantitative Analysis of Fault Distributions in Complex Software Systems, IEEE Transactions on Software Engineering, v.33 n.5, p.273-286, May 2007 [doi>10.1109/TSE.2007.1005]
• 2: L.C. Briand and I. Wieczorek, Resource estimation in software engineering, Encyclopedia of Software Engineering, (ed. J. Marcinak), Wiley, 2002. 1160--1196.
• 3: Jerome R. Busemeyer , Yi-Min Wang, Model comparisons and model selections based on generalization criterion methodology, Journal of Mathematical Psychology, v.44 n.1, p.171-189, March 2000 [doi>10.1006/jmps.1999.1282]
• 4: J. Cohen, Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, Lawrence Erlbaum Associates, 2003.
• 5: Eclipse bug dataset, available at the MSR 2008 Mining Challenge website http://msr.uwaterloo.ca/msr2008/.
• 6: Norman E. Fenton , Niclas Ohlsson, Quantitative Analysis of Faults and Failures in a Complex Software System, IEEE Transactions on Software Engineering, v.26 n.8, p.797-814, August 2000 [doi>10.1109/32.879815]
• 7: Hemant Joshi , Chuanlei Zhang , S. Ramaswamy , Coskun Bayrak, Local and Global Recency Weighting Approach to Bug Prediction, Proceedings of the Fourth International Workshop on Mining Software Repositories, p.33, May 20-26, 2007 [doi>10.1109/MSR.2007.17]
• 8: Tim Menzies , Jeremy Greenwald , Art Frank, Data Mining Static Code Attributes to Learn Defect Predictors, IEEE Transactions on Software Engineering, v.33 n.1, p.2-13, January 2007 [doi>10.1109/TSE.2007.10]
• 9: Rao, C. R., Prediction of Future Observations in Growth Curve Models, Statistical Science, vol. 2(4), 434--447, 1987.
• 10: Hongyu Zhang , Xiuzhen Zhang, Comments on "Data Mining Static Code Attributes to Learn Defect Predictors", IEEE Transactions on Software Engineering, v.33 n.9, p.635-637, September 2007 [doi>10.1109/TSE.2007.70706]
• 11: Hongyu Zhang , Xiuzhen Zhang , Ming Gu, Predicting Defective Software Components from Code Complexity Measures, Proceedings of the 13th Pacific Rim International Symposium on Dependable Computing, p.93-96, December 17-19, 2007 [doi>10.1109/PRDC.2007.56]
• 12: Hongyu Zhang, On the Distribution of Software Faults, IEEE Transactions on Software Engineering, v.34 n.2, p.301-302, March 2008 [doi>10.1109/TSE.2007.70771]

INDEX TERMS

Primary Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.8 Metrics
          Subjects: Product metrics

Additional Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.8 Metrics
          Subjects: Process metrics

General Terms:
Measurement, Reliability

Keywords:
defect growth model, defect prediction, eclipse, polynomial regression