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

Abstract

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.

Learning Time Acceleration in Support Vector Regression: A Case Study in Educational Data Mining

“…The use of SVR has been less common for educational purposes. López-Martín et al [10] used SVR with linear kernel to predict the productivity of higher-education graduate students. Indeed, support vector models have been used successfully to solve numerous other complex problems [3], such as flight control [11], security [12], genomics [13], cancer prediction [14,15], facial recognition [16], predicting solar and wind energy resources [17], and predicting academic dropouts [18], among others.…”

Incremental Granular Model Improvement Using Particle Swarm Optimization

“…Drouard [8] proposed a model to estimate the head posture of a robot using linear regression. Martin [9] proposed a model that predicts graduate student productivity using vector regression support (SVR). Amikhani [10] proposed a model to predict the performance of solar power plants using an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system.…”