PREDICTING DEFECTS IN A LARGE TELECOMMUNICATION SYSTEM

Gözde Koçak, Burak Turhan, Ayşe Bener

2008

Abstract

In a large software system knowing which files are most likely to be fault-prone is valuable information for project managers. However, our experience shows that it is difficult to collect and analyze fine-grained test defects in a large and complex software system. On the other hand, previous research has shown that companies can safely use cross company data with nearest neighbor sampling to predict their defects in case they are unable to collect local data. In this study we analyzed 25 projects of a large telecommunication system. To predict defect proneness of modules we learned from NASA MDP data. We used static call graph based ranking (CGBR) as well as nearest neighbor sampling for constructing method level defect predictors. Our results suggest that, for the analyzed projects, at least 70% of the defects can be detected by inspecting only i) 6% of the code using a Naïve Bayes model, ii) 3% of the code using CGBR framework.

References

  1. Bell, R.M., Ostrand, T.J., Weyuker, E.J., July 2006. Looking for Bugs in All the Right Places. Proc. ACM/International Symposium on Software Testing and Analysis (ISSTA2006), Portland, Maine, pp. 61- 71.
  2. Boetticher, G., Menzies, T., Ostrand, T., 2007. PROMISE Repository of empirical software engineering data http://promisedata.org/repository, West Virginia University, Department of Computer Science.
  3. Fenton N.E. and Neil M., A critique of software defect prediction models. IEEE Transactions On Software Engineering (1999) vol. 25 pp. 675-689
  4. Fenton, N.E., Ohlsson, N., Aug 2000. Quantitative Analysis of Faults and Failures in a Complex Software System. IEEE Trans. on Software Engineering, Vol 26, No 8, pp.797-814.
  5. Kocak, G., Turhan, B., Bener, A., 2008. Software Defect Prediction Using Call Graph Based Ranking (CGBR) Framework, to appear in Proceedings of EUROMICRO SPPI (2008), Parma, Italy.
  6. Koru, A. G., Liu, H., 2005. An Investigation of the Effect of Module Size on Defect Prediction Using Static Measures. Proceeding of PROMISE 2005, St. Louis, Missouri, pp. 1-6.
  7. Koru, A. G., Liu, H., Nov.-Dec. 2005. Building effective defect-prediction models in practice Software, IEEE, vol. 22, Issue 6, pp. 23 - 29.
  8. Malaiya, Y. K., Denton, J., 2000. Module Size Distribution and Defect Density, ISSRE 2000, pp. 62- 71.
  9. Menzies, T., Greenwald, J., Frank, A., 2007. Data Mining Static Code Attributes to Learn Defect Predictors, IEEE Transactions on Software Engineering, 33, no.1, 2-13.
  10. Menzies, T., Turhan, B., Bener, A., Distefano, J., 2007. “Cross- vs within-company defect prediction studies”, Technical report, Computer Science, West Virginia University.
  11. Ostrand, T.J., Weyuker., E.J., July 2002. The Distribution of Faults in a Large Industrial Software System. Proc. ACM/International Symposium on Software Testing and Analysis (ISSTA2002), Rome, Italy, pp. 55-64.
  12. Ostrand, T.J., Weyuker, E.J., Bell, R.M., July 2004. Where the Bugs Are. Proc. ACM/International Symposium on Software Testing and Analysis (ISSTA2004), Boston, MA.
  13. Ostrand, T.J., Weyuker, E.J., Bell, R.M., April 2005. Predicting the Location and Number of Faults in Large Software Systems. IEEE Trans. on Software Engineering, Vol 31, No 4.
  14. Ostrand, T.J., Weyuker, E.J., Bell, R.M., July 2007. Automating Algorithms for the Identification of FaultProne Files. Proc. ACM/International Symposium on Software Testing and Analysis (ISSTA07), London, England.
  15. Turhan, B., Bener, A., 2008. Data Sampling for Cross Company Defect Predictors, Technical Report, Computer Engineering, Bogazici University.
  16. Turhan , B., Bener, A., A Multivariate Analysis of Static Code Attributes for Defect Prediction. Quality Software, 2007. QSIC 7807. Seventh International Conference on (2007) pp. 231 - 237
  17. Nagappan, N. and Ball T., Explaining failures using software dependences and churn metrics. Technical Report, Microsoft Research (2006)
  18. Zhang, H., On the Distribution of Software Faults. Software Engineering, IEEE Transactions on (2008) vol. 34 (2) pp. 301-302
  19. Zimmermann, T., Nagappan, N. Predicting Subsystem Failures using Dependency Graph Complexities. Technical Report, Microsoft Research (2006).
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Paper Citation


in Harvard Style

Koçak G., Turhan B. and Bener A. (2008). PREDICTING DEFECTS IN A LARGE TELECOMMUNICATION SYSTEM . In Proceedings of the Third International Conference on Software and Data Technologies - Volume 2: ICSOFT, ISBN 978-989-8111-52-4, pages 284-288. DOI: 10.5220/0001887502840288


in Bibtex Style

@conference{icsoft08,
author={Gözde Koçak and Burak Turhan and Ayşe Bener},
title={PREDICTING DEFECTS IN A LARGE TELECOMMUNICATION SYSTEM},
booktitle={Proceedings of the Third International Conference on Software and Data Technologies - Volume 2: ICSOFT,},
year={2008},
pages={284-288},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001887502840288},
isbn={978-989-8111-52-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Software and Data Technologies - Volume 2: ICSOFT,
TI - PREDICTING DEFECTS IN A LARGE TELECOMMUNICATION SYSTEM
SN - 978-989-8111-52-4
AU - Koçak G.
AU - Turhan B.
AU - Bener A.
PY - 2008
SP - 284
EP - 288
DO - 10.5220/0001887502840288