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Automatic search term identification for change tasks


Kevic, Katja; Fritz, Thomas (2014). Automatic search term identification for change tasks. In: 36th International Conference on Software Engineering, New Ideas and Emerging Results (NIER), Hyderabad, 31 May 2014 - 7 June 2014.

Abstract

At the beginning of a change task, software developers search the source code to locate the places relevant to the task. As previous research and a small exploratory study that we conducted show, developers perform poorly in identifying good search terms and therefore waste a lot of time querying and exploring irrelevant code. To support developers in this step, we present an approach to automatically identify good search terms. Based on existing work and an analysis of change tasks, we derived heuristics, determined their relevancy and used the results to develop our approach. For a preliminary evaluation, we conducted a study with ten developers working on open source change tasks. Our approach was able to identify good search terms for all tasks and outperformed the searches of the participants, illustrating the potential of our approach. In addition, since the used heuristics are solely based on textual features of change tasks, our approach is easy and generally applicable and can leverage much of the existing work on feature location.

Abstract

At the beginning of a change task, software developers search the source code to locate the places relevant to the task. As previous research and a small exploratory study that we conducted show, developers perform poorly in identifying good search terms and therefore waste a lot of time querying and exploring irrelevant code. To support developers in this step, we present an approach to automatically identify good search terms. Based on existing work and an analysis of change tasks, we derived heuristics, determined their relevancy and used the results to develop our approach. For a preliminary evaluation, we conducted a study with ten developers working on open source change tasks. Our approach was able to identify good search terms for all tasks and outperformed the searches of the participants, illustrating the potential of our approach. In addition, since the used heuristics are solely based on textual features of change tasks, our approach is easy and generally applicable and can leverage much of the existing work on feature location.

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3 citations in Web of Science®
5 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:7 June 2014
Deposited On:12 Mar 2014 08:21
Last Modified:17 Aug 2017 18:34
Publisher:IEEE
ISBN:978-1-4503-2768-8
Publisher DOI:https://doi.org/10.1145/2591062.2591117
Related URLs:http://2014.icse-conferences.org/NIER
Other Identification Number:merlin-id:9299

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