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- W2783446640 abstract "Software engineering (SE) has been defined as the application of a systematic, disciplined, quantifiable approach to the development, operation, and maintenance of software. Enhancement is a type of software maintenance. SE involves software planning (SP), and SP includes prediction. In this study, we propose the application of two types of support vector regression (SVR) termed e-SVR and ν-SVR to predict the duration of the software enhancement. A SVR is a type of support vector machine, which is a machine learning technique. Two data sets of software projects were used for training and testing the e-SVR and ν-SVR. The prediction accuracy of the SVRs was compared to that of a statistical regression. Based on statistical tests, results showed that a e-SVR with linear kernel was statistically better than that of a statistical regression model when software projects were enhanced on Mid Range platform and coded in programming languages of third generation." @default.
- W2783446640 created "2018-01-26" @default.
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- W2783446640 date "2017-12-01" @default.
- W2783446640 modified "2023-10-13" @default.
- W2783446640 title "Support Vector Regression for Predicting the Enhancement Duration of Software Projects" @default.
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- W2783446640 doi "https://doi.org/10.1109/icmla.2017.0-101" @default.
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