A Method of Fault Fix Priority Identification for Open Source Project
Hironobu Sone1, Yoshinobu Tamura2, Shigeru Yamada3
1Hironobu Sone*, Graduate School of Integrative Science and Engineering, Tokyo City University, Tokyo, Japan.
2Yoshinobu Tamura, Department of Intelligent Systems, Faculty of Knowledge Engineering, Tokyo City University, Tokyo, Japan.
3Shigeru Yamada, Graduate School of Engineering, Tottori University, Tottori-shi, Japan.

Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 2396-2400 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7153118419/2019©BEIESP | DOI: 10.35940/ijrte.D7153.118419

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Open source software are adopted as embedded systems, server usage because of quick delivery, cost reduction and standardization of systems. Many open source software are developed under the peculiar development style known as bazaar method. According to this method, faults are detected and fixed by developers around the world, and the fixed result will be reflected in the next release. Also, the fix time of faults tends to be shorter as the development of open source software progresses. However, several large-scale open source projects have a problem that faults fixing takes a lot of time because the faults corrector cannot handle many faults reports quickly. In this paper, we aim to identify the fix priority of newly registered faults in the bug tracking system by using random forest, and we make an index to detect the faults that require high fix priority and long fault fixing time when faults are reported in specific version of open source project. The index is derived and identified by using open source project data obtained from bug tracking system. In addition, we try to improve the detection accuracy of the proposed index by learning not only the specific version but also the fault report data of the past version by using random forest considering the characteristic similarities of faults fix among different versions. As a result, the detection accuracy has highly improved comparing with using only specific version data and using logistic regression.
Keywords: Open Source Software, Fault Identification, Random Forest, Software Effort, Fault Big Data
Scope of the Article: Advanced Computing Architectures and New Programming Models.