期刊名称：Walailak Journal of Science and Technology (WJST)
印刷版ISSN：2228-835X
出版年度：2015
卷号：13
期号：4
DOI：10.14456/vol13iss3pp
语种：English
出版社：Institute of Research and Development, Walailak University.
摘要：In the software industry, it is a big challenge to measure the development effort and time for developing the software’s. It comprising the several phases to measure the development effort and time, but measuring the effort in each phase creates problems. It is also observed that estimation of the effort for developing a project may be over-estimated and under-estimated. It can be lead an enormous damage to the organization with respect to budget and schedule. So, to address the aforementioned, a cognitive technique is proposed for measuring the development effort, time and errors. After measuring the development effort, machine learning techniques: Bays Net, Logistic Regression, Multi-perceptron, SMO and Lib-SVM are applied for software defects prediction. To estimate the software development effort and defects, NASA PROMISE CM1, KC3, PC1, PC2, and JM1 datasets and devised datasets (proposed cognitive technique parameters of original datasets) are used. The experimental result of both the experiments proves the goodness of the proposed work of this paper.
关键词：Engineering;Cognitive weight; basic control structures; operators; operands; machine learning techniques
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