Comparison of big data analyses for reliable open source software
Yoshinobu Tamura, Shigeru Yamada · 2016
Open source software are used in wide ranging areas of software system development, because of the standardization, cost reduction, quick delivery. Many open source software are useful for the software developer and software managers to develop the software system quickly. Also, the open source software are characterized by the bug tracking system (BTS). The BTS's such as Bugzilla are controlled by almost open source projects. In the BTS, many data sets are recorded by project members and software users. In this paper, we compare the methods of big fault data analyses based on the deep learning and neural network. Moreover, we show several numerical examples of big fault data analyses in the actual open source software project. As the effectiveness analysis of the proposed method, the comparison results of recognition rate in terms of the proposed method and the conventional method are shown in this paper.