Stochastic Effort Optimization Analysis for OSS Projects

Abstract

It is very important to produce and maintain a reliable system structured from several open-source software, because many open-source software (OSS) have been introduced in various software systems. As for the OSS development paradigm, the bug tracking systems have been used for software quality management in many OSS projects. It will be helpful for OSS project managers to assess the reliability and effort management of OSS, if many fault data recorded on the bug tracking system are analyzed for software quality improvement. In this chapter, we focus on a method of stochastic effort optimization analysis for OSS projects by using the OSS fault big data. Then, we discuss the method of effort estimation based on stochastic differential equation and jump-diffusion process. In particular, we use the OSS development effort data obtained from fault big data. Then, deep learning is used for the parameter estimation of jump-diffusion process model. Also, we discuss the optimal maintenance problem based on our methods. Moreover, several numerical examples of the proposed methods are shown by using the effort data in the actual OSS projects. Furthermore, we discuss the results of numerical examples based on our methods of effort optimization.