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http://dx.doi.org/10.5516/NET.04.2011.023

RISKY MODULE PREDICTION FOR NUCLEAR I&C SOFTWARE  

Kim, Young-Mi (Korea Institute of Nuclear Safety)
Kim, Hyeon-Soo (Dept. of Computer Science & Engineering, Chungnam Nat'l Univ.)
Publication Information
Nuclear Engineering and Technology / v.44, no.6, 2012 , pp. 663-672 More about this Journal
Abstract
As software based digital I&C (Instrumentation and Control) systems are used more prevalently in nuclear plants, enhancement of software dependability has become an important issue in the area of nuclear I&C systems. Critical attributes of software dependability are safety and reliability. These attributes are tightly related to software failures caused by faults. Software testing and V&V (Verification and Validation) activities are hence important for enhancing software dependability. If the risky modules of safety-critical software can be predicted, it will be possible to focus on testing and V&V activities more efficiently and effectively. It should also make it possible to better allocate resources for regulation activities. We propose a prediction technique to estimate risky software modules by adopting machine learning models based on software complexity metrics. An empirical study with various machine learning algorithms was executed for comparing the prediction performance. Experimental results show SVMs (Support Vector Machines) perform as well or better than the other methods.
Keywords
Machine Learning; Safety-critical Software; Software Complexity Metrics; Software Dependability; Software Testing;
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  • Reference
1 Karim O. Elish, Mahmoud O. Elish, Predicting defectprone software modules using support vector machines, The Journal of Systems and Software 81(2008) 649-660   DOI   ScienceOn
2 Mark A. Hall, Correlation-based Feature Selection for Descrete and Numeric Class Machine Learning, Proceedings of the 17'th International Conference on Machine Learning, 2000
3 IEEE Std. 1012, IEEE Standard for Software Verification and Validation, 2004
4 Witten, I., Frank, E., Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, San Francisco, 2005.
5 http://sw-assurance.gsfc.nasa.gov/disciplines/reliability/index.php
6 B. Boehm. Software Engineering Economics. Prentice-Hall, 1981.
7 H. Drucker, D. Wu, and V. N. Vapnik, Support vector machines for spam categorization, IEEE Transactions on Neural Networks, vol. 10, no. 5, pp. 1048-1054, 1999.   DOI   ScienceOn
8 Burges, C., A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery, 1998
9 T. Joachims, Making large-Scale SVM Learning Practical. Advances in Kernel Methods - Support Vector Learning, B. Scholkopf and C. Burges and A. Smola (ed.), MIT-Press, 1999
10 Steve R, Gunn, Support Vector Machines for Classification and Regression, Technical Report, University of Southampton, 10 May 1998
11 Tom M. Mitchell, Machine Learning, McGRAW-Hill, 1997
12 Jiawei Han, Data Mining: Concepts and Techniques, University of Illinois at Urnaba-Champaign, Elsevier
13 Pang-Ning Tan, Michael Steinbach and Vipin Kumar, Introduction to Data Mining, Addison Wesley
14 Taghi M. Khoshgoftaar and John C. Monson, Prediction Software Development Errors Using Software Complexity Metrics, IEEE Journal on Selected Areas in Communications. Vol. 8, No. 2, Feb 1990
15 I. Gondra, Applying machine learning to software faultproness prediction, The Journal of Systems and Software 81 (2008) 186-195   DOI   ScienceOn
16 Bellman, Adaptive Control Processes, Princeton University Press.
17 Taghi M. Khoshgoftaar and Kalai S. Kalaichelvan, Detection of Fault-Prone Program Modules in a Very Large Telecommunications System, 16th International Symposium on Software Reliability Engineering, 1995
18 Tiong-Hwee Goh, Semantic Extraction Using Neural Network Modelling and Sensitivity Analysis, Proceedings of International Joint Conference on Neural Networks, 1993
19 Michael R. Lyu, Handbook of Software Reliability Engineering, McGraw-Hill
20 Briand, L.T., Basili, V.R., Hetmanski, C., Developing interpretable models for optimized set reduction for identifying high-risk software components. IEEE Transactions on Software Engineering SE-19(11), 1028-1034, 1993.
21 Khoshgoftaar, T.M., Munson, J.C., Prediction software development errors using complexity metrics. IEEE Journal on Selected Areas in Communications 8 (2), 153-261, 1990
22 Porter, A., Selby, R., Empirically guided software development using metric-based classification trees. IEEE Software 7(2), 46-54, 1990.
23 T.J. McCabe. A complexity measure. IEEE Transactions on Software Engineering, 2(4):308-320, Dec. 1976.   DOI   ScienceOn
24 Khoshgoftaar, T.M., Lanning, D.L., Pandya, A.S., A comparative study of pattern recognition techniques for quality evaluation of telecommunications software, IEEE Journal on Selected Areas in Communications 12 (2), 208-217, 1994.
25 Xing, F., Guo, P., Lyu, M.R., A novel method for early software quality prediction based on support vector machine, Proceedings of IEEE International Conference on Software Reliability Engineering, pp. 213-222, 2005
26 Young-Mi Kim, Choong-Heui Jeong, A-Rang Jeong and Hyeon Soo Kim, Risky Module Estimation in Safety-Critical Software, IEEE/ACIS International Conference on Computer and Information Science, 2009,6