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• W1979241035 abstract "There always has been a demand to produce efficient and high quality software. There are various object oriented metrics that measure various properties of the software like coupling, cohesion, inheritance etc. which affect the software to a large extent. These metrics can be used in predicting important quality attributes such as fault proneness, maintainability, effort, productivity and reliability. Early prediction of fault proneness will help us to focus on testing resources and use them only on the classes which are predicted to be fault-prone. Thus, this will help in early phases of software development to give a measurement of quality assessment. This paper provides the review of the previous studies which are related to software metrics and the fault proneness. In other words, it reviews several journals and conference papers on software fault prediction. There is large number of software metrics proposed in the literature. Each study uses a different subset of these metrics and performs the analysis using different datasets. Also, the researchers have used different approaches such as Support vector machines, naive bayes network, random forest, artificial neural network, decision tree, logistic regression etc. Thus, this study focuses on the metrics used, dataset used and the evaluation or analysis method used by various authors. This review will be beneficial for the future studies as various researchers and practitioners can use it for comparative analysis." @default.
• W1979241035 created "2016-06-24" @default.
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• W1979241035 date "2011-09-30" @default.
• W1979241035 modified "2023-10-16" @default.
• W1979241035 title "Software fault prediction for object oriented systems" @default.
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• W1979241035 doi "https://doi.org/10.1145/2020976.2020991" @default.
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