The misuse of the NASA Metrics Data Program data sets for automated software defect prediction

IET Seminar Digest

Volumn 2011, Issue 1, 2011, Pages 96-103

  Gray, D ^a   Bowes, D ^a   Davey, N ^a   Sun, Yi ^a   Christianson, B ^a  

^a UNIVERSITY OF HERTFORDSHIRE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CODE METRICS; DATA CLEANSING; DATA POINTS; DATA SETS; DEFECT PREDICTION; METRICS DATA PROGRAMS; PRE-PROCESSING; SOFTWARE DEFECT PREDICTION; TRAINING AND TESTING;

DEFECTS; EXPERIMENTS; FORECASTING; NASA; SOFTWARE ENGINEERING;

COMPUTER SOFTWARE SELECTION AND EVALUATION;

EID: 82955251102     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1049/ic.2011.0012     Document Type: Conference Paper

References (29)

1. Bezerra, M., Oliveira, A. & Meira, S. (2007), A constructive rbf neural network for estimating the probability of defects in software modules, pp. 2869-2874.
2. Boetticher, G. (2006), Improving credibility of machine learner models in software engineering, in 'Advanced Machine Learner Applications in Software Engineering', Software Engineering and Knowledge Engineering.
3. Chawla, N. V., Japkowicz, N. & Kolcz, A. (2004), 'Special issue on learning from imbalanced datasets', SIGKDD Explor. Newsl. 6(1).
4. Elish, K. O. & Elish, M. O. (2008), 'Predicting defect-prone software modules using support vector machines', J. Syst. Softw. 81(5), 649-660. (Pubitemid 351389562)
5. Halstead, M. H. (1977), Elements of Software Science (Operating and programming systems series), Elsevier Science Inc., New York, NY, USA.
6. Jiang, Y. & Cukic, B. (2009), Misclassification cost-sensitive fault prediction models, in 'Proceedings of the 5th International Conference on Predictor Models in Software Engineering', PROMISE '09, ACM, New York, NY, USA, pp. 20:1-20:10.
7. Jiang, Y., Cukic, B. & Menzies, T. (2008), Can data transformation help in the detection of fault-prone modules?, in 'DEFECTS '08: Proceedings of the 2008 workshop on Defects in large software systems', ACM, New York, NY, USA, pp. 16-20.
8. Kaminsky, K. & Boetticher, G. (2004), Building a genetically engineerable evolvable program (GEEP) using breadth-based explicit knowledge for predicting software defects, pp. 10-15 Vol.1.
9. Khoshgoftaar, T. M. & Allen, E. B. (2003), 'Ordering fault-prone software modules', Software Quality Control 11, 19-37. (Pubitemid 39019689)
10. Khoshgoftaar, T. M. & Seliya, N. (2004), The necessity of assuring quality in software measurement data, in 'METRICS '04: Proceedings of the Software Metrics, 10th International Symposium', IEEE Computer Society, Washington, DC, USA, pp. 119-130.
11. Kim, S., Zimmermann, T., Pan, K. & Whitehead, E. J. J. (2006), Automatic identification of bug-introducing changes, in 'ASE '06: Proceedings of the 21st IEEE/ACM International Conference on Automated Software Engineering', IEEE Computer Society, Washington, DC, USA, pp. 81-90.
12. Kołcz, A., Chowdhury, A. & Alspector, J. (2003), Data duplication: an imbalance problem?, in 'ICML 2003 Workshop on Learning from Imbalanced Datasets,'.
13. Koru, A. G. & Liu, H. (2005), 'An investigation of the effect of module size on defect prediction using static measures', ACM SIGSOFT Software Engineering Notes 30(4), 1-5.
14. Lessmann, S., Baesens, B., Mues, C. & Pietsch, S. (2008), 'Benchmarking classification models for software defect prediction: A proposed framework and novel findings', Software Engineering, IEEE Transactions on 34(4), 485-496.
15. Li, Z. & Reformat, M. (2007), 'A practical method for the software fault-prediction', Information Reuse and Integration, 2007. IRI 2007. IEEE International Conference on pp. 659-666.
16. Liebchen, G. A. & Shepperd, M. (2008), Data sets and data quality in software engineering, in 'PROMISE '08: Proceedings of the 4th international workshop on Predictor models in software engineering', ACM, New York, NY, USA, pp. 39-44.
17. McCabe, T. J. (1976), A complexity measure, in 'ICSE '76: Proceedings of the 2nd international conference on Software engineering', IEEE Computer Society Press, Los Alamitos, CA, USA, p. 407.
18. Mende, T. & Koschke, R. (2009), Revisiting the evaluation of defect prediction models, in 'Proceedings of the 5th International Conference on Predictor Models in Software Engineering', PROMISE '09, ACM, New York, NY, USA, pp. 7:1-7:10.
19. Menzies, T., Dekhtyar, A., Distefano, J. & Greenwald, J. (2007), 'Problems with precision: A response to "comments on 'data mining static code attributes to learn defect predictors"", IEEE Transactions on Software Engineering 33, 637-640. (Pubitemid 47322970)
20. Menzies, T., Greenwald, J. & Frank, A. (2007), 'Data mining static code attributes to learn defect predictors', Software Engineering, IEEE Transactions on 33(1), 2-13. (Pubitemid 46002165)
21. Menzies, T., Milton, Z., Turhan, B., Cukic, B., Jiang, Y. & Bener, A. (2010), 'Defect prediction from static code features: current results, limitations, new approaches', Automated Software Engg. 17(4), 375-407.
22. Menzies, T. & Stefano, J. S. D. (2004), 'How good is your blind spot sampling policy?', High-Assurance Systems Engineering, IEEE International Symposium on 0, 129-138.
23. Menzies, T., Stefano, J. S. D., Orrego, A. & Chapman, R. (2004), 'Assessing predictors of software defects', Proceedings of Workshop on Predictive Software Models .
24. Nickerson, A. S., Japkowicz, N. & Milios, E. (2001), Using unsupervised learning to guide resampling in imbalanced data sets, in 'In Proceedings of the Eighth International Workshop on AI and Statitsics', pp. 261-265.
25. Pelayo, L. & Dick, S. (2007), Applying novel resampling strategies to software defect prediction, pp. 69-72.
26. Seliya, N., Khoshgoftaar, T. & Zhong, S. (2005), Analyzing software quality with limited fault-proneness defect data, pp. 89-98.
27. Tosun, A. & Bener, A. (2009), Reducing false alarms in software defect prediction by decision threshold optimization, in 'Proceedings of the 2009 3rd International Symposium on Empirical Software Engineering and Measurement', ESEM '09, IEEE Computer Society, Washington, DC, USA, pp. 477-480.
28. Turhan, B., Menzies, T., Bener, A. B. & Di Stefano, J. (2009), 'On the relative value of cross-company and within-company data for defect prediction', Empirical Softw. Engg. 14, 540-578.
29. Witten, I. H. & Frank, E. (2005), Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann Series in Data Management Systems, second edn, Morgan Kaufmann.