A Dataset of High Impact Bugs: Manually-Classified Issue Reports

Submitted by msquire on Tue, 2015-07-21 19:15

Title	A Dataset of High Impact Bugs: Manually-Classified Issue Reports
Publication Type	Conference Proceedings
Year of Publication	2015
Authors	Ohira, M, Kashiwa, Y, Yamatani, Y, Yoshiyuki, H, Maeda, Y, Limsettho, N, Fujino, K, Hata, H, Ihara, A, Matsumoto, K
Secondary Title	12th Working Conference on Mining Software Repositories (MSR 2015)
Date Published	05/2015
Publisher	IEEE
Keywords	ambari, camel, derby, wicket
Abstract	The importance of supporting test and maintenance activities in software development has been increasing, since recent software systems have become large and complex. Although in the field of Mining Software Repositories (MSR) there are many promising approaches to predicting, localizing, and triaging bugs, most of them do not consider impacts of each bug on users and developers but rather treat all bugs with equal weighting, excepting a few studies on high impact bugs including security, performance, blocking, and so forth. To make MSR techniques more actionable and effective in practice, we need deeper understandings of high impact bugs. In this paper we introduced our dataset of high impact bugs which was created by manually reviewing four thousand issue reports in four open source projects (Ambari, Camel, Derby and Wicket).
URL	http://oss.sys.wakayama-u.ac.jp/publications/pman3.cgi?DOWNLOAD=141
Full Text