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Support vector regression for predicting the productivity of higher education graduate students from individually developed software projects

机译：支持向量回归，可通过单独开发的软件项目预测高等教育研究生的生产率

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Productivity prediction of a software engineer is necessary to determine whether corrective actions are needed and to identify improvement options to produce better results. It can be performed from abstraction levels such as organisation, team project, individual project, or task. Software engineering education and training has approached its efforts at individual level. In this study, the authors propose the application of a data mining technique named support vector regression (SVR) to predict the productivity of individuals (i.e. graduate students). Its prediction accuracy was compared with that of a statistical regression model, and with those of two neural networks. After applying a Wilcoxon statistical test, results suggest that an SVR with linear kernel using new and changed lines of code, and programming language experience as independent variables, could be used for predicting the individual productivity of a higher education graduate student, when software projects coded in either Java or C++ programming languages, have been developed by following a disciplined process specifically proposed for academic environments.
机译：必须确定软件工程师的生产率，才能确定是否需要采取纠正措施并确定改进方案以产生更好的结果。可以从组织，团队项目，单个项目或任务等抽象级别执行。软件工程教育和培训已在个人层面上进行了努力。在这项研究中，作者提出了一种名为支持向量回归（SVR）的数据挖掘技术来预测个人（即研究生）的生产率。将其预测准确性与统计回归模型的预测准确性以及两个神经网络的预测准确性进行了比较。在进行了Wilcoxon统计检验后，结果表明，当使用软件项目进行编码时，具有使用新的和更改的代码行以及编程语言经验作为自变量的线性内核的SVR可以用于预测高等教育研究生的个人生产力。按照Java或C ++编程语言编写的程序，是通过遵循专门针对学术环境建议的纪律程序开发的。

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