Prediction and Ranking of Fault-Prone Software Modules
Large and complex software systems are developed by integrating various independent modules. It is important to ensure quality of these modules through independent testing where modules are tested and faults are removed as soon as failures are experienced. System failures due to the software failure are common and result in undesirable consequences. Moreover, it is difficult to produce fault-free software due to problem complexity, complexity of human behavior, and the resource constrains. This chapter presents a noval approach for prediction and ranking of the software module using classification and fuzzy ordering algoritms.
KeywordsFuzzy Inference System Software Module Flow Graph Fault Prediction Software Metrics
- Catal, C., & Diri, B. (2008). A fault prediction model with limited fault data to improve test process. In Proceedings of the 9th International Conference on Product Focused Software Process Improvement (pp. 244–257).Google Scholar
- Fenton, N. E., & Neil, M. (2000). Software metrics: roadmap. In Proceedings of the Conference on The Future of Software Engineering (pp. 375–370). Limerick, Ireland.Google Scholar
- Han, J., & Kamber, M. (2001). Data mining: concepts and techniques. USA: Morgan Kaufmann Publishers.Google Scholar
- Kumar, K. S. (2009). Early Software Reliability and Quality Prediction, Ph.D. Thesis, IIT Kharagpur, Kharagpur, India.Google Scholar
- Musa, J. D., Iannino, A., Okumoto, K. (1987). Software reliability: measurement, prediction, and application. New York: McGraw–Hill Publication.Google Scholar
- NASA (2004). NASA metrics data program, http://mdp.ivv.nasa.gov/.
- Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1, 81–106.Google Scholar
- Ross, T. J. (2005). Fuzzy logic with engineering applications (2nd ed.). India: Willy.Google Scholar
- Zadeh, L. A. (1965). Fuzzy Sets: Information and Control, 8(3), 338–353.Google Scholar