A Method of User-oriented Reliability Assessment for Open Source Software and Its Applications

Software development environment has been changing into new development paradigms such as concurrent distributed development environment and the so-called open source project by using network computing technologies. In case of considering the effect of the debugging process on an entire system in the development of a method of reliability assessment for open source project, it is necessary to grasp the deeply-intertwined factors, such as programming path, size of each component, skill of fault reporter, and so on. In order to consider the effect of each software component on the reliability of an entire system under such new distributed development paradigm, we propose a new approach to software reliability assessment by creating a fusion of neural network and software reliability growth model. In this paper, we show application examples of software reliability assessment based on neural network and software reliability growth model for open source software. Also, we analyze actual software fault count data to show numerical examples of software reliability assessment for the open source software. Then, we consider the efficiency and effectiveness of the software reliability assessment method for the actual open source software.