Exploring the relationship of history characteristics and defect count: an empirical study

ABSTRACT

During the lifetime of a project, a huge amount of information is generated, e.g. in versioning systems or bug data bases. When analysed appropriately, the knowledge about the previous project characteristics allows estimating the project's future evolution. For example, it is very valuable to know particular history characteristics of a file indicating its fault proneness because it helps testers to focus their testing effort on these specific files. In this paper, we present the results of an empirical study, exploring the relationship between history characteristics of software entities and their defects. For this purpose, we analyze 9 open source java projects. The results show that there are some history characteristics that highly correlate with defects in software, e.g. the number of changes and the number of distinct authors performing changes to a file. The number of co-changed files does not correlate with the defect count.

REFERENCES

• 1: Erik Arisholm , Lionel C. Briand, Predicting fault-prone components in a java legacy system, Proceedings of the 2006 ACM/IEEE international symposium on Empirical software engineering, September 21-22, 2006, Rio de Janeiro, Brazil [doi>10.1145/1159733.1159738]
• 2: Victor R. Basili , Lionel C. Briand , Walcélio L. Melo, A Validation of Object-Oriented Design Metrics as Quality Indicators, IEEE Transactions on Software Engineering, v.22 n.10, p.751-761, October 1996 [doi>10.1109/32.544352]
• 3: Robert M. Bell , Thomas J. Ostrand , Elaine J. Weyuker, Looking for bugs in all the right places, Proceedings of the 2006 international symposium on Software testing and analysis, July 17-20, 2006, Portland, Maine, USA [doi>10.1145/1146238.1146246]
• 4: Giovanni Denaro , Sandro Morasca , Mauro Pezzè, Deriving models of software fault-proneness, Proceedings of the 14th international conference on Software engineering and knowledge engineering, July 15-19, 2002, Ischia, Italy [doi>10.1145/568760.568824]
• 5: Giovanni Denaro , Mauro Pezzè, An empirical evaluation of fault-proneness models, Proceedings of the 24th International Conference on Software Engineering, May 19-25, 2002, Orlando, Florida [doi>10.1145/581339.581371]
• 6: Michael Fischer , Martin Pinzger , Harald Gall, Populating a Release History Database from Version Control and Bug Tracking Systems, Proceedings of the International Conference on Software Maintenance, p.23, September 22-26, 2003
• 7: Todd L. Graves , Alan F. Karr , J. S. Marron , Harvey Siy, Predicting Fault Incidence Using Software Change History, IEEE Transactions on Software Engineering, v.26 n.7, p.653-661, July 2000 [doi>10.1109/32.859533]
• 8: International Software Testing Qualifications Board. 2005. ISTQB Standard Glossary of Terms used in Software Testing VI.1.
• 9: Taghi M. Khoshgoftaar , Edward B. Allen , Robert Halstead , Gary P. Trio , Ronald M. Flass, Using Process History to Predict Software Quality, Computer, v.31 n.4, p.66-72, April 1998 [doi>10.1109/2.666844]
• 10: Nachiappan Nagappan , Thomas Ball, Use of relative code churn measures to predict system defect density, Proceedings of the 27th international conference on Software engineering, May 15-21, 2005, St. Louis, MO, USA [doi>10.1145/1062455.1062514]
• 11: Nachiappan Nagappan , Thomas Ball , Andreas Zeller, Mining metrics to predict component failures, Proceedings of the 28th international conference on Software engineering, May 20-28, 2006, Shanghai, China [doi>10.1145/1134285.1134349]
• 12: Ohlsson, M. C., von Mayrhauser, A., McGuire, B., Wohlin, C. 1999. Code Decay Analysis of Legacy Software through Successive Releases. Proceedings of IEEE Aerospace Conference, pp 69--81.
• 13: Thomas J. Ostrand , Elaine J. Weyuker , Robert M. Bell, Where the bugs are, Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis, July 11-14, 2004, Boston, Massachusetts, USA
• 14: Thomas J. Ostrand , Elaine J. Weyuker , Robert M. Bell, Predicting the Location and Number of Faults in Large Software Systems, IEEE Transactions on Software Engineering, v.31 n.4, p.340-355, April 2005 [doi>10.1109/TSE.2005.49]
• 15: Ranjith Purushothaman , Dewayne E. Perry, Toward Understanding the Rhetoric of Small Source Code Changes, IEEE Transactions on Software Engineering, v.31 n.6, p.511-526, June 2005 [doi>10.1109/TSE.2005.74]
• 16: Schröter, A., Zimmermann, T. Premraj, R., and R., Zeller, A. If Your Bug Database Could Talk. 2006. In Proceedings of the 5th International Symposium on Empirical Software Engineering. (Rio de Janeiro, Brazil, September 2006), Volume II: Short Papers and Posters, pp. 18--20, 2006.
• 17: Elaine J. Weyuker , Thomas J. Ostrand , Robert M. Bell, Using Developer Information as a Factor for Fault Prediction, Proceedings of the Third International Workshop on Predictor Models in Software Engineering, p.8, May 20-26, 2007 [doi>10.1109/PROMISE.2007.14]
• 18: Claes Wohlin , Per Runeson , Martin Höst , Magnus C. Ohlsson , Bjöorn Regnell , Anders Wesslén, Experimentation in software engineering: an introduction, Kluwer Academic Publishers, Norwell, MA, 2000

INDEX TERMS

Primary Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.7 Distribution, Maintenance, and Enhancement

Additional Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.8 Metrics

Keywords:
defect database, empirical study, versioning systems