Abstract
Software systems are becoming increasingly important these days with the development of computer. Many countries and companies are investing much more in developing software systems to implement a lot of significant assignment, so the quality and reliability of the software needs to be assured. Hence, the characteristics of the source code of these systems need to be measured to obtain more information about it, and software metrics is needed to be analyzed. This paper introduces a hybrid learning method that incorporate with genetic algorithm and decision tree algorithm in order to evolve optimal subsets of software metrics for risk prediction during the early phase of the software life-circle. Experimental results are presented which illustrate the feasibility and improved performance of our approach when compared with using all metrics for risk prediction by decision tree.
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Xu, Z., Yang, B., Guo, P. (2007). Software Risk Prediction Based on the Hybrid Algorithm of Genetic Algorithm and Decision Tree. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. ICIC 2007. Communications in Computer and Information Science, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74282-1_30
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DOI: https://doi.org/10.1007/978-3-540-74282-1_30
Publisher Name: Springer, Berlin, Heidelberg
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