Prediction of faults-slip-through in large software projects: An empirical evaluation

Software Quality Journal

Volumn 22, Issue 1, 2014, Pages 51-86

  Afzal, Wasif ^a   Torkar, Richard ^b,c   Feldt, Robert ^b,c   Gorschek, Tony ^c  

^a BAHRIA UNIVERSITY   (Pakistan)

^b CHALMERS UNIVERSITY OF TECHNOLOGY   (Sweden)

^c BLEKINGE INSTITUTE OF TECHNOLOGY   (Sweden)

Author keywords

Empirical; Faults slip through; Prediction; Search based

Indexed keywords

FORECASTING; GENETIC PROGRAMMING; NEURAL NETWORKS; PARTICLE SWARM OPTIMIZATION (PSO);

ARTIFICIAL IMMUNE RECOGNITION SYSTEM; EMPIRICAL; EMPIRICAL EVALUATIONS; FAULTS-SLIP-THROUGH; GENE EXPRESSION PROGRAMMING; PREDICTION TECHNIQUES; SEARCH-BASED; SOFTWARE TEST PROCESS;

SOFTWARE TESTING;

EID: 84893926141     PISSN: 09639314     EISSN: 15731367     Source Type: Journal    
DOI: 10.1007/s11219-013-9205-3     Document Type: Article

References (61)

1. Afzal, W. (2009). Search-based approaches to software fault prediction and software testing. Blekinge Institute of Technology Licentiate Series No. 2009: 06, Ronneby, Sweden.
2. Afzal, W. (2010). Using faults-slip-through metric as a predictor of fault-proneness: Proceedings of the 21st Asia Pacific Software Engineering Conference (APSEC'10), IEEE.
3. Afzal, W., & Torkar, R. (2008). A comparative evaluation of using genetic programming for predicting fault count data: Proceedings of the 3rd International Conference on Software Engineering Advances (ICSEA'08), IEEE.
4. Afzal, W., Torkar, R., Feldt, R., & Wikstrand, G. (2010). Search-based prediction of fault-slip-through in large software projects: Proceedings of the 2nd International Symposium on Search-Based Software Engineering (SSBSE'10), IEEE Computer Society. pp. 79-88.
5. Aha, D. W., Kibler, D., & Albert, M. K. (1991). Instance-based learning algorithms. Machine Learning, 6, 37-66.
6. Antolić, Ž. (2007). Fault slip through measurement process implementation in CPP software implementation: Proceedings of the 30th Jubilee International Convention (MIPRO'07). Ericsson Nikola Tesla.
7. Arisholm, E., Briand, L. C., & Johannessen, E. B. (2010). A systematic and comprehensive investigation of methods to build and evaluate fault prediction models. Journal of Systems and Software, 83(1), 2-17.
8. Blickle, T. (1996). Theory of evolutionary algorithms and application to system synthesis. PhD thesis. Zurich, Switzerland: Swiss Federal Institute of Technology.
9. Boehm, B., & Basili, V. R. (2001). Software defect reduction top 10 list. Computer, 34(1), 135-137.
10. Breiman, L. (1996). Bagging predictors. Machine Learning, 24, 123-140.
11. Briand, L., Emam, K., Freimut, B., & Laitenberger, O. (2000). A comprehensive evaluation of capture-recapture models for estimating software defect content. IEEE Transactions on Software Engineering, 26(6).
12. Canu S, Grandvalet Y, Guigue V, & Rakotomamonjy A (2005) SVM and kernel methods toolbox. Perception Systémes et Information, INSA de Rouen, Rouen, France.
13. Catal, C., & Diri, B. (2009). A systematic review of software fault prediction studies. Expert Systems with Applications, 36(4), 7346-7354.
14. Challagulla, V., Bastani, F., Yen, I., & Paul, R. (2005). Empirical assessment of machine learning based software defect prediction techniques. Proceedings of the 10th IEEE workshop on object oriented real-time dependable systems.
15. Cleary, J. G., & Trigg, L. E. (1995). K*: An instance-based learner using an entropic distance measure. 12th International Conference on Machine Learning (ICML'95).
16. Damm, L. O. (2007). Early and cost-effective software fault detection-Measurement and implementation in an industrial setting. PhD thesis, Blekinge Institute of Technology.
17. Damm, L. O., Lundberg, L., & Wohlin, C. (2006). Faults-slip-through-a concept for measuring the efficiency of the test process. Software Process: Improvement & Practice, 11(1), 47-59.
18. Fenton, N. E., & Neil, M. (1999). A critique of software defect prediction models. IEEE Transactions on Software Engineering, 25(5), 675-689.
19. Ferreira, C. (2001). Gene expression programming: A new adaptive algorithm for solving problems. Complex Systems, 13(2).
20. Gyimothy, T., Ferenc, R., & Siket, I. (2005). Empirical validation of object-oriented metrics on open source software for fault prediction. IEEE Transactions on Software Engineering, 31(10). 897-910.
21. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009), The WEKA data mining software: An update. SIGKDD Explorations Newsletter, 11(1), 10-18.
22. Harman, M. (2007). The current state and future of search based software engineering. Proceeding of the Future of Software Engineering (FOSE'07). Washington, DC, USA: IEEE Computer Society, pp. 342-357.
23. Harman, M. (2010). The relationship between search based software engineering and predictive modeling: Proceedings of the 6th International Conference on Predictive Models in Software Engineering (PROMISE'10). New York, NY, USA: ACM.
24. Harman, M., & Jones, B. (2001). Search-based software engineering. Information and Software Technology, 43(14), 833-839.
25. Hribar, L. (2008) Implementation of FST in design phase of the project: Proceedings of the 31st Jubilee International Convention (MIPRO'08). Ericsson Nikola Tesla.
26. Hughes, R. T. (1996). Expert judgement as an estimating method. Information and Software Technology, 38(2), 67-75.
27. IEEE. (1990) IEEE standard glossary of software engineering terminology-IEEE Std 610. 12-1990. Standards Coordinating Committee of the Computer Society of the IEEE, IEEE Standards Board. New York, USA: The Institute of Electrical and Electronic Engineers, Inc.
28. Ioannidis, J. P. A. (2005) Why most published research findings are false. PLoS Medicine, 2(8), 696-701.
29. Jha, G. K., Thulasiraman, P., & Thulasiram, R. K. (2009). PSO based neural network for time series forecasting: International Joint Conference on Neural Networks.
30. Jørgensen, M., Kirkebøen, G., Sjøberg, D. I. K., Anda, B., & Bratthall, L. (2000). Human judgement in effort estimation of software projects: Proceedings of the workshop on using multi-disciplinary approaches in empirical software engineering research, co-located with ICSE'00. Ireland: Limerick.
31. Juristo, N., & Moreno, A. M. (2001). Basics of software engineering experimentation. Dordrecht: Kluwer Academic Publishers.
32. Kachigan, S. K. (1982). Statistical analysis-an interdisciplinary introduction to univariate and multivariate methods. New York: Radius Press.
33. Kitchenham, B., Pickard, L., MacDonell, S., & Shepperd, M. (2001). What accuracy statistics really measure? IEE Proceedings Software, 148(3), 81-85.
34. Lavesson, N., & Davidsson, P. (2008). Generic methods for multi-criteria evaluation: Proceedings of the SIAM International Conference on Data Mining (SD'08).
35. Lessmann, S., Baesens, B., Mues, C., & Pietsch, S. (2008). Benchmarking classification models for software defect prediction: A proposed framework and novel findings. IEEE Transactions on Software Engineering, 34(4), 485-496.
36. Liu, Y., Khoshgoftaar, T., & Seliya, N. (2010). Evolutionary optimization of software quality modeling with multiple repositories. IEEE Transactions on Software Engineering (Article in print).
37. Lyu, M. R. (ed) (1996). Handbook of software reliability engineering. Hightstown, NJ: McGraw-Hill Inc.
38. Mohagheghi, P., Conradi, R., Killi, O. M., & Schwarz, H. (2004). An empirical study of software reuse vs. defect-density and stability: Proceedings of the 26th International Conference on Software Engineering (ICSE'04). Washington, DC, USA: IEEE Computer Society.
39. Nagappan, N., & Ball, T. (2005). Static analysis tools as early indicators of pre-release defect density: Proceedings of the 27th international conference on Software engineering (ICSE'05). New York, NY, USA: ACM.
40. Nagappan, N., Murphy, B., & Basili, V. (2008). The influence of organizational structure on software quality: An empirical case study: Proceedings of the 30th International Conference on Software Engineering (ICSE'08). New York, NY, USA: ACM.
41. Pickard, L., Kitchenham, B., & Linkman, S. (1999). An investigation of analysis techniques for software datasets. In: Proceedings of the 6th International Software Metrics Symposium (METRICS'99). Los Alamitos, USA: IEEE Computer Society.
42. Poli, R., Langdon, W. B., & McPhee, N. F. (2008). A field guide to genetic programming. Published via http://lulu. com and freely available at http://www. gp-field-guide. org. uk.
43. Rakitin, S. R. (2001). Software verification and validation for practitioners and managers (2nd ed.). 685 Canton Street, Norwood, MA, USA: Artech House., Inc.
44. Rodriguez, J. J., Kuncheva, L. I., & Alonso, C. J. (2006). Rotation forest: A new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28, 1619-1630.
45. Runeson, P., Andersson, C., Thelin, T., Andrews, A., & Berling, T. (2006). What do we know about defect detection methods? IEEE Software, 23(3), 82-90.
46. Russell S., & Norvig P. (2003) Artificial intelligence-a modern approach. Prentice Hall Series in Artificial Intelligence, USA.
47. Shepperd, M., Cartwright, M., & Kadoda, G. (2000). On building prediction systems for software engineers. Empirical Software Engineering, 5(3), 175-182.
48. STD. (2008) IEEE standard 12207-2008 systems and software engineering-Software life cycle processes. Software and systems engineering standards committee of the IEEE computer society. New York, USA: The Institute of Electrical and Electronic Engineers, Inc.
49. Staron, M., & Meding, W. (2008). Predicting weekly defect inflow in large software projects based on project planning and test status. Information & Software Technology, 50(7-8), 782-796.
50. Tian, J. (2004). Quality-evaluation models and measurements. IEEE Software, 21(3), 84-91.
51. Tomaszewski, P., Håkansson, J., Grahn, H., & Lundberg, L. (2007). Statistical models vs. expert estimation for fault prediction in modified code-an industrial case study. Journal of Systems and Software, 80(8), 1227-1238.
52. Trelea, I. C. (2003). The PSO algorithm: Convergence analysis and parameter selection. IP Letters, 85(6).
53. Veevers, A., & Marshall, A. C. (1994). A relationship between software coverage metrics and reliability. Software Testing, Verification and Reliability, 4(1), 3-8.
54. Wagner, S. (2006). A literature survey of the quality economics of defect-detection techniques: Proceedings of the ACM/IEEE International Symposium on Empirical Software Engineering (ISESE'06).
55. Wang, Y. (2000). A new approach to fitting linear models in high dimensional spaces. PhD thesis. New Zealand: Department of Computer Science, University of Waikato.
56. Wang, Y, & Witten, I. H. (1996). Induction of model trees for predicting continuous classes. Technical report, University of Waikato, Department of Computer Science, Hamilton, New Zealand, URL http://www. cs. waikato. ac. nz/pubs/wp/1996/uow-cs-wp-1996-23. pdf.
57. Watkins, A., Timmis, J., & Boggess, L. (2004). Artificial immune recognition system (AIRS): An immune-inspired supervised learning algorithm. Genetic programming and Evolvable Machines, 5(3), 291-317.
58. Weka Documentation (2010) Class REPTree. Tree. URL http://www. dbs. ifi. lmu. de/~zimek/diplomathesis/implementations/EHNDs/doc/weka/classifiers/trees/REPTree. Tree. html.
59. Weyuker, E. J., Ostrand, T. J., & Bell, R. M. (2010). Comparing the effectiveness of several modeling methods for fault prediction. Empirical Software Engineering, 15(3), 277-295.
60. Witten I, Frank E (2005) Data mining-practical machine learning tools and techniques. USA: Morgan-Kaufmann Publishers.
61. Zhong, S., Khoshgoftaar, T. M., & Seliya, N. (2004). Unsupervised learning for expert-based software quality estimation: Proceedings of the 8th IEEE International Symposium on High Assurance Systems Engineering (HASE'04).