A dataset of high impact bugs: Manually-classified issue reports

Masao Ohira, Yutaro Kashiwa, Yosuke Yamatani, Hayato Yoshiyuki, Yoshiya Maeda, Nachai Limsettho, Keisuke Fujino, Hideaki Hata, Akinori Ihara, Kenichi Matsumoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Citations (Scopus)

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).

Original languageEnglish
Title of host publicationProceedings - 12th Working Conference on Mining Software Repositories, MSR 2015
PublisherIEEE Computer Society
Pages518-521
Number of pages4
ISBN (Electronic)9780769555942
DOIs
Publication statusPublished - Aug 4 2015
Externally publishedYes
Event12th Working Conference on Mining Software Repositories, MSR 2015 - Florence, Italy
Duration: May 16 2015May 17 2015

Publication series

NameIEEE International Working Conference on Mining Software Repositories
Volume2015-August
ISSN (Print)2160-1852
ISSN (Electronic)2160-1860

Other

Other12th Working Conference on Mining Software Repositories, MSR 2015
CountryItaly
CityFlorence
Period5/16/155/17/15

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Software

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