Software reliability prediction model based on support vector regression with improved estimation of distribution algorithms

• To maintain the diversity of the population can improve the performance of the software reliability prediction model, which confirms the improved EDA is effective.
• In this paper, a method to optimize the SVR parameters is proposed. This method can avoid the blindness of the SVR parameter selection and provide a technical means for the better use of SVR.
• Software reliability prediction is very important for software engineering. In this paper, an intelligent approach is adopted in order to achieve software reliability prediction and obtain good performance.

Software reliability prediction plays a very important role in the analysis of software quality and balance of software cost. The data during software lifecycle is used to analyze and predict software reliability. However, predicting the variability of software reliability with time is very difficult. Recently, support vector regression (SVR) has been widely applied to solve nonlinear predicting problems in many fields and has obtained good performance in many situations; however it is still difficult to optimize SVR's parameters. Previously, some optimization algorithms have been used to find better parameters of SVR, but these existing algorithms usually are not fully satisfactory. In this paper, we first improve estimation of distribution algorithms (EDA) in order to maintain the diversity of the population, and then a hybrid improved estimation of distribution algorithms (IEDA) and SVR model, called IEDA-SVR model, is proposed. IEDA is used to optimize parameters of SVR, and IEDA-SVR model is used to predict software reliability. We compare IEDA-SVR model with other software reliability models using real software failure datasets. The experimental results show that the IEDA-SVR model has better prediction performance than the other models.

Figure optionsDownload as PowerPoint slide