Accurate and reliable software development effort estimation (SDEE) is one of the main concerns for project managers. Planning and scheduling a software projects using and inaccurate estimate may cause severe risks to software project under development such as delayed delivery, poor quality software, missing features. Therefore, accurate prediction of software effort plays an important role in the minimization of these risks that can lead to projects failure. Nowadays, application of artificial intelligence techniques has grown dramatically for predicting software effort. The researchers found that these techniques are suitable tools for accurate prediction. In this study, an improved model is designed for estimating software effort using support vector regression (SVR) and two feature selection (FS) methods. Prior to building model step, a preprocessing stage is performed by random forest or Boruta feature selection methods to remove unimportant features. Next, the SVR model is tuned by a grid search approach. The performance of model is then evaluated over eight well-known datasets through 30%holdout validation method. To show the impact of feature selection on the accuracy of SVR model, the proposed model was compared with SVR model without feature selection. The results indicated that SVR with feature selection outperforms SVR without FS in terms of the three accuracy measures used in this empirical study.
Improving Software Development effort estimating using Support Vector Regression and Feature Selection
