Support Vector Regression Based on Grid-Search Method for Agile Software Effort Prediction

The existing literature on software development effort estimation is extensive and focuses particularly on traditional software projects, while few studies have been devoted to agile projects, especially estimating the effort needed for completing the whole software projects. Story points and team velocity are among the commonly effort drivers used for planning and predicting when a software will be completed. In this paper, we propose an improved model for estimating the software effort based on support vector regression (SVR) optimized by grid search method (GS). The story point and velocity were used as inputs of the prediction model. The empirical evaluation is carried out using 21 historical agile software projects through leave-one-out cross validation method. The obtained results demonstrate that our approach was able to improve the performance of SVR technique. Moreover, it outperforms, in terms of Pred(0.25), MMRE and MdMRE, some recent methods reported in the recent literature.