Abstract
With the increasing popularity of mobile applications, a light-weighted bug tracking systems has been widely needed. While the high release frequency of the mobile applications requires a rapid bug tracking system for the software maintenance, the needs for users’ feedback can be easily accessed and manipulated for both common users and developers, which motivates us to develop a mobile service for bug tracking, namely Mubug, by combining the natural language processing technique and machine learning technique. Project managers can easily configure and setup bug tracking service without any installation on Mubug. Reporters can submit bug reports with texts, voices or images using their mobile devices. Bug reports can thus be processed and assigned to developers automatically. In this paper, we present the architecture of Mubug and some implememtation details.
创新点
基于微信公众号接口建立一个快速的软件缺陷处理服务。通过引入微信接口、自然语言处理技术和机器学习技术,辅助移动应用软件缺陷的处理流程。基于广泛应用的微信客户端,面向终端普通用户,提供了错误报告提交接口,用户可以通过语音,图片,文字三种方式提交错误报告;相关工作人员可以通过微信端更新和确认错误报告处理状态。
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References
Just S, Premraj R, Zimmermann T. Towards the next generation of bug tracking systems. In: Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing, Herrsching am Ammersee, 2008. 82–85
Zhang W, Nie L, Jiang H, et al. Developer social networks in software engineering: construction, analysis, and application. Sci China Inf Sci, 2014, 57: 121101
Zimmermann T, Premraj R, Sillito J, et al. Improving bug tracking systems. In: 31st International Conference on Software Engineering—Companion Volume, Vancouver, 2009. 247–250
Chen Z, Luo B. Quasi-crowdsourcing testing for educational projects. In: Companion Proceedings of the 36th International Conference on Software Engineering. New York: ACM, 2014. 272–275
Feng Y, Chen Z, Jones J A, et al. Test report prioritization to assist crowdsourced testing. In: Proceedings of the 10th Joint Meeting on Foundations of Software Engineering. New York: ACM, 2015. 225–236
Foo S, Li H. Chinese word segmentation and its effect on information retrieval. Inf Process Manag, 2004, 40: 161–190
Kao A, Poteet S R. Natural Language Processing and Text Mining. London: Springer-Verlag, 2007
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Feng, Y., Liu, Q., Dou, M. et al. Mubug: a mobile service for rapid bug tracking. Sci. China Inf. Sci. 59, 1–5 (2016). https://doi.org/10.1007/s11432-015-5506-4
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DOI: https://doi.org/10.1007/s11432-015-5506-4