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

© The Institution of Engineering and Technology
Received 08/12/2016, Accepted 17/07/2017, Revised 20/06/2017, Published 31/07/2017

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.

Inspec keywords: Java; computer science education; statistical testing; training; software engineering; C++ language; regression analysis; support vector machines; further education; data mining

Other keywords: software engineering education; SVR; higher education graduate student productivity; data mining technique; linear kernel; software engineering training; higher education graduate student prediction; programming language experience; support vector regression; software engineer productivity prediction; C++ programming languages; academic environments; statistical regression model; software projects; Java; neural networks; Wilcoxon statistical test

Subjects: Software engineering techniques; Other topics in statistics; Computing education and training; Knowledge engineering techniques; Data handling techniques

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