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Collaborative bug triaging using textual similarities and change set analysis


Kevic, Katja; Müller, Sebastian; Fritz, Thomas; Gall, Harald (2013). Collaborative bug triaging using textual similarities and change set analysis. In: 6th International Workshop on Cooperative and Human Aspects of Software Engineering, San Francisco, California, USA, 25 May 2013 - 25 May 2013, 17-25.

Abstract

Bug triaging assigns a bug report, which is also known as a work item, an issue, a task or simply a bug, to the most appropriate software developer for fixing or implementing it. However, this task is tedious, time-consuming and error-prone if not supported by effective means. Current techniques either use information retrieval and machine learning to find the most similar bugs already fixed and recommend expert developers, or they analyze change information stemming from source code to propose expert bug solvers. Neither technique combines textual similarity with change set analysis and thereby exploits the potential of the interlinking between bug reports and change sets. In this paper, we present our approach to identify potential experts by identifying similar bug reports and analyzing the associated change sets. Studies have shown that effective bug triaging is done collaboratively in a meeting, as it requires the coordination of multiple individuals, the understanding of the project context and the understanding of the specific work practices. Therefore, we implemented our approach on a multi-touch table to allow multiple stakeholders to interact simultaneously inthe bug triaging and to foster their collaboration. In the current stage of our experiments we have experienced that the expert recommendations are more specific and useful when the rationale behind the expert selection is also presented to the users.

Abstract

Bug triaging assigns a bug report, which is also known as a work item, an issue, a task or simply a bug, to the most appropriate software developer for fixing or implementing it. However, this task is tedious, time-consuming and error-prone if not supported by effective means. Current techniques either use information retrieval and machine learning to find the most similar bugs already fixed and recommend expert developers, or they analyze change information stemming from source code to propose expert bug solvers. Neither technique combines textual similarity with change set analysis and thereby exploits the potential of the interlinking between bug reports and change sets. In this paper, we present our approach to identify potential experts by identifying similar bug reports and analyzing the associated change sets. Studies have shown that effective bug triaging is done collaboratively in a meeting, as it requires the coordination of multiple individuals, the understanding of the project context and the understanding of the specific work practices. Therefore, we implemented our approach on a multi-touch table to allow multiple stakeholders to interact simultaneously inthe bug triaging and to foster their collaboration. In the current stage of our experiments we have experienced that the expert recommendations are more specific and useful when the rationale behind the expert selection is also presented to the users.

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5 citations in Web of Science®
8 citations in Scopus®
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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Event End Date:25 May 2013
Deposited On:19 Mar 2013 14:06
Last Modified:07 Dec 2017 20:40
Publisher:IEEE
ISBN:978-1-4673-6290-0
Related URLs:http://www.chaseresearch.org/workshops/chase2013
https://www.conference-publishing.com/list.php?Event=ICSEWS13CHASE
Other Identification Number:merlin-id:8059

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