Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30172
A Study on Early Prediction of Fault Proneness in Software Modules using Genetic Algorithm

Authors: Parvinder S. Sandhu, Sunil Khullar, Satpreet Singh, Simranjit K. Bains, Manpreet Kaur, Gurvinder Singh

Abstract:

Fault-proneness of a software module is the probability that the module contains faults. To predict faultproneness of modules different techniques have been proposed which includes statistical methods, machine learning techniques, neural network techniques and clustering techniques. The aim of proposed study is to explore whether metrics available in the early lifecycle (i.e. requirement metrics), metrics available in the late lifecycle (i.e. code metrics) and metrics available in the early lifecycle (i.e. requirement metrics) combined with metrics available in the late lifecycle (i.e. code metrics) can be used to identify fault prone modules using Genetic Algorithm technique. This approach has been tested with real time defect C Programming language datasets of NASA software projects. The results show that the fusion of requirement and code metric is the best prediction model for detecting the faults as compared with commonly used code based model.

Keywords: Genetic Algorithm, Fault Proneness, Software Faultand Software Quality.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1075440

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1528

References:


[1] Saida Benlarbi,Khaled El Emam, Nishith Geol (1999), "Issues in Validating Object-Oriented Metrics for Early Risk Prediction", by Cistel Technology 210 Colonnade Road Suite 204 Nepean, Ontario Canada K2E 7L5.
[2] Fenton, N. E. and Neil, M. (1999), "A Critique of Software Defect Prediction Models", Bellini, I. Bruno, P. Nesi, D. Rogai, University of Florence, IEEE Trans. Softw. Engineering, vol. 25, Issue no. 5, pp. 675- 689.
[3] Bellini, P. (2005), "Comparing Fault-Proneness Estimation Models", 10th IEEE International Conference on Engineering of Complex Computer Systems (ICECCS'05), vol. 0, 2005, pp. 205-214.
[4] Giovanni Denaro (2000), "Estimating Software Fault-Proneness for Tuning Testing Activities" Proceedings of the 22nd International Conference on Software Engineering (ICSE2000), Limerick, Ireland, June 2000.
[5] Mahaweerawat, A. (2004), "Fault-Prediction in object oriented software-s using neural network techniques", Advanced Virtual and Intelligent Computing Center (AVIC), Department of Mathematics, Faculty of Science, Chulalongkorn University, Bangkok, Thailand, pp. 1-8.
[6] Ma, Y., Guo, L. (2006), "A Statistical Framework for the Prediction of Fault-Proneness", West Virginia University, Morgantown.
[7] Thomas Zimmermann, Nachiappan Nagappan (2008), " Predicting Defects Using Social Network Analysis on Dependency Graphs", International Conference on Software Engineering (ICSE 2008), Leipzig, Germany.
[8] Audris Mockus, Nachiappan Nagappan and Trung T.Dinh-Trong, (2009) "Test Coverage and Post-Verification Defects: A Multiple Case Study," ACM-IEEE Empirical Software Engineering and Measurement Conference (ESEM), Orlando, FL, 2009.
[9] Cagatay Catal & Banu Diri (2009), "A Systematic Review of Software Fault Prediction Studies" Journal of Expert Systems with Applications, Volume 36, Issue 4, May 2009.
[10] Jonas Boberg (2008), "Early Fault Detection with the Model-based Testing" , 7th ACM SIGNPLAN workshop on ERLANG, 2008.
[11] Bindu Goel & Yogesh Singh (2008),"Emperical Investigation of Metrics for Fault Prediction on Object Oriented Software" the Book series in Computational Intelligence, 2008.
[12] Khoshgoftaar, T. M., Allen, E. B., Ross, F. D., Munikoti, R., Goel, N. & Nandi, A. (1997), "Predicting fault-prone modules with case-based reasoning". ISSRE 1997, the Eighth International Symposium on Software Engineering (pp. 27-35), IEEE Computer Society (1997).
[13] Min-Gu Lee and Theresa L. Jefferson (2005), "An Empirical Study of Software Maintenance of a Web-based Java Application", Proceedings of the 21st IEEE International Conference on Software Maintenance (ICSM-05), IEEE (2005).
[14] Marco D' Ambros and Michle Lanza (2006), "Software Bugs and Evolution: A Visual Approach to uncover their relationship", Proceedings of IEEE Conference on Software Maintenance and Reegineering (CSMR' 06), IEEE (2006).
[15] George E. Stark (1996), "Measurements for Managing Software Maintenance", IEEE computer Society, 1996.
[16] Khoshgoftaar, T.M. and Munson, J.C. (1990), "Predicting Software Development Errors using Complexity Metrics", Selected Areas in Communications, IEEE Journal on, Volume: 8 Issue: 2, Feb. 1990, Page(s): 253 -261.
[17] Menzies, T., Ammar, K., Nikora, A., and Stefano, S. (2003), "How Simple is Software Defect Prediction?" Submitted to Journal of Empirical Software Engineering, October (2003).
[18] Eman, K., Benlarbi, S., Goel, N., and Rai, S. (2001), "Comparing casebased reasoning classifiers for predicting high risk software components", Systems Software, Journal of, Volume: 55 Issue: 3, Nov. (2001), Page(s): 301 - 310.
[19] Fenton, N.E. and Neil, M. (1999), "A critique of software defect prediction models", Software Engineering, IEEE Transactions on, Volume: 25 Issue: 5, Sept.- Oct. 1999, Page(s): 675 -689.
[20] Khoshgoftaar, T. M. and Seliya, N. (2002), "Tree-based software quality estimation models for fault prediction", METRICS 2002, the Eighth IIIE Symposium on Software Metrics (pp. 203-214). IEEE Computer Society 2002.
[21] Seliya N., Khoshgoftaar, T.M., Zhong S. (2005), "Analyzing software quality with limited fault-proneness defect data", Ninth IEEE international Symposium on 12-14 Oct, 2005.
[22] Lan Guo, Bojan Cukic, Harshinder Singh (2003), "Predicting Fault Prone Modules by the Dempster-Shafer Belief Networks," ase, pp.249, 18th IEEE International Conference on Automated Software Engineering (ASE'03), 2003.
[23] NASA IV &V Facility. Metric Data Program. Available from http: //MDP.ivv.nasa.gov/.
[24] Jiang Y., Cukic B. and Menzies T. (2007), "Fault Prediction Using Early Lifecycle Data". ISSRE 2007, the 18th IEEE Symposium on Software Reliability Engineering, IEEE Computer Society, Sweden, pp. 237-246.
[25] Hall M. A. (1998), Correlation-based Feature Subset Selection for Machine Learning. Hamilton, New Zealand, 1998.
[26] Challagulla V.U.B., Bastani F.B., Yen I. L. and Paul (2005) "Empirical assessment of machine learning based software defect prediction techniques", 10th IEEE International Workshop on Object-Oriented Real-Time Dependable Systems, USA, pp. 263-270