Machine Learning Classification to Effort Estimation for Embedded Software Development Projects

Support Vector Regression

SVR uses the same principles as SVM for classification, albeit with a few minor differences. The -SVR (Alex & Bernhard, 2004) regression method uses an -insensitive loss function to solve regression problems. This approach attempts to find a continuous function in which as many data points as possible lie within the -wide insensitivity tube.-SVR is used to estimate the amount of effort required for software projects (Oliveira, 2006). This approach has been tested using the well-known NASA software project dataset (John & Victor, 1981; Shin & Goel, 2000). However, these studies did not investigate the parameters of -SVR. The effectiveness of the SVM (and SVR) using the resulting continuous function depends on the kernel parameter () and soft margin parameter (C) (Cortes & Vapnik, 1995). In addition, the value of affects the estimations given by -SVR.

We proposed a three-dimensional grid search to find the most appropriate combination of these parameters (Iwata, Liebman, Stone, Nakashima, Anan & Ishii, 2015). Our method improved the mean magnitude of relative error (MMRE, see Equation (3) in the section “Evaluation Criteria”) from 0.165 (Cortes & Vapnik, 1995) to 0.149 using leave-one-out cross-validation (Shin & Goel, 2000).