Does measuring code change improve fault prediction?

ACM International Conference Proceeding Series

Volumn , Issue , 2011, Pages

  Bell, Robert M ^a   Ostrand, Thomas J ^a   Weyuker, Elaine J ^a  

^a AT AND T LABS RESEARCH   (United States)

Author keywords

Code churn; Empirical study; Fault prediction; Fault percentile average; Software faults

Indexed keywords

CODE CHURN; EMPIRICAL STUDIES; FAULT PREDICTION; FAULT-PERCENTILE AVERAGE; SOFTWARE FAULTS;

FORECASTING; MATHEMATICAL MODELS; MODELS; PREDICTIVE CONTROL SYSTEMS; SOFTWARE ENGINEERING; SOFTWARE TESTING;

PROGRAM DEBUGGING;

EID: 80054077408     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2020390.2020392     Document Type: Conference Paper

References (14)

1. T.L. Graves, A.F. Karr, J.S. Marron, and H. Siy. Predicting Fault Incidence Using Software Change History. IEEE Trans. on Software Engineering, Vol 26, No. 7, July 2000, pp. 653-661.
2. T.M. Khoshgoftaar, E.B. Allen, J. Deng. Using Regression Trees to Classify Fault-Prone Software Modules. IEEE Trans. on Reliability, Vol 51, No. 4, Dec 2002, pp. 455-462.
3. T. Menzies, J. Greenwald and A. Frank. Data Mining Static Code Attributes to Learn Defect Predictors. IEEE Trans. Software Eng., 33(1), pp. 2-13, 2007.
4. T. Menzies, B. Turhan, A. Bener, G. Gay, B. Cukic and Y. Jiang. Implications of Ceiling Effects in Defect Predictors. Proc. 4th Int. Workshop on Predictor Models in Software Engineering (PROMISE08), pp. 47-54, 2008.
5. A. Mockus and D.M. Weiss. Predicting Risk of Software Changes. Bell Labs Technical Journal, April-June 2000, pp. 169-180.
6. N. Nagappan and T. Ball. Use of Relative Code Churn Measures to Predict System Defect Density. Proc. 27th Int. Conference on Software Engineering (ICSE05), 2005.
7. N. Nagappan and T. Ball. Using Software Dependencies and Churn Metrics to Predict Field Failures: An Empirical Case Study. Proc. Empirical Software Engineering and Measurement Conference (ESEM), Madrid, Spain, 2007.
8. N. Nagappan, A. Zeller, T. Zimmermann, K. Herzig, and B. Murphy. Change Bursts as Defect Predictors. Proc. 21st IEEE Int. Symposium on Software Reliability Engineering (ISSRE2010).
9. N. Ohlsson and H. Alberg. Predicting Fault-Prone Software Modules in Telephone Switches. IEEE Trans. on Software Engineering, Vol 22, No 12, December 1996, pp. 886-894.
10. T.J. Ostrand, E.J. Weyuker, and R.M. Bell. Programmer-based Fault Prediction. Proc. Int. Conference on Predictive Models (PROMISE10), Timisoara, Romania, September 2010.
11. T.J. Ostrand, E.J. Weyuker, and R.M. Bell. Predicting the Location and Number of Faults in Large Software Systems. IEEE Trans. on Software Engineering, Vol 31, No 4, April 2005.
12. E.J. Weyuker, T.J. Ostrand, and R.M. Bell. Do Too Many Cooks Spoil the Broth? Using the Number of Developers to Enhance Defect Prediction Models. Empirical Software Eng., Vol 13, No. 5, October 2008.
13. E.J. Weyuker, T.J. Ostrand, and R.M. Bell. Comparing the Effectiveness of Several Modeling Methods for Fault Prediction. Empirical Software Eng. Vol 15, No. 3, June 2010.
14. T. Zimmermann and N. Nagappan. Predicting Defects Using Network Analysis on Dependency Graphs. Proc. 13th Int. Conference on Software Engineering (ICSE08), p.531-540, 2008.