Active Code Search: Incorporating User Feedback to Improve Code Search Relevance

Code search techniques return relevant code fragments given a user query. They typically work in a passive mode: given a user query, a static list of code fragments sorted by the relevance scores decided by a code search technique is returned to the user. A user will go through the sorted list of re...

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Main Authors: Wang, Shaowei, LO, David, JIANG, Lingxiao
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Language:English
Published: Institutional Knowledge at Singapore Management University 2014
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Online Access:https://ink.library.smu.edu.sg/sis_research/2424
https://ink.library.smu.edu.sg/context/sis_research/article/3424/viewcontent/p677_wang.pdf
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-34242015-11-14T09:51:06Z Active Code Search: Incorporating User Feedback to Improve Code Search Relevance Wang, Shaowei LO, David JIANG, Lingxiao Code search techniques return relevant code fragments given a user query. They typically work in a passive mode: given a user query, a static list of code fragments sorted by the relevance scores decided by a code search technique is returned to the user. A user will go through the sorted list of returned code fragments from top to bottom. As the user checks each code fragment one by one, he or she will naturally form an opinion about the true relevance of the code fragment. In an active model, those opinions will be taken as feedbacks to the search engine for refining result lists. In this work, we incorporate users’ opinion on the results from a code search engine to refine result lists: as a user forms an opinion about one result, our technique takes this opinion as feedback and leverages it to re-order the results to make truly relevant results appear earlier in the list. The re- finement results can also be cached to potentially improve future code search tasks. We have built our active refinement technique on top of a state-of-the-art code search engine— Portfolio. Our technique improves Portfolio in terms of Normalized Discounted Cumulative Gain (NDCG) by more than 11.3%, from 0.738 to 0.821. 2014-09-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2424 info:doi/10.1145/2642937.2642947 https://ink.library.smu.edu.sg/context/sis_research/article/3424/viewcontent/p677_wang.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Code Search User Feedback Active Learning Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Code Search
User Feedback
Active Learning
Software Engineering
spellingShingle Code Search
User Feedback
Active Learning
Software Engineering
Wang, Shaowei
LO, David
JIANG, Lingxiao
Active Code Search: Incorporating User Feedback to Improve Code Search Relevance
description Code search techniques return relevant code fragments given a user query. They typically work in a passive mode: given a user query, a static list of code fragments sorted by the relevance scores decided by a code search technique is returned to the user. A user will go through the sorted list of returned code fragments from top to bottom. As the user checks each code fragment one by one, he or she will naturally form an opinion about the true relevance of the code fragment. In an active model, those opinions will be taken as feedbacks to the search engine for refining result lists. In this work, we incorporate users’ opinion on the results from a code search engine to refine result lists: as a user forms an opinion about one result, our technique takes this opinion as feedback and leverages it to re-order the results to make truly relevant results appear earlier in the list. The re- finement results can also be cached to potentially improve future code search tasks. We have built our active refinement technique on top of a state-of-the-art code search engine— Portfolio. Our technique improves Portfolio in terms of Normalized Discounted Cumulative Gain (NDCG) by more than 11.3%, from 0.738 to 0.821.
format text
author Wang, Shaowei
LO, David
JIANG, Lingxiao
author_facet Wang, Shaowei
LO, David
JIANG, Lingxiao
author_sort Wang, Shaowei
title Active Code Search: Incorporating User Feedback to Improve Code Search Relevance
title_short Active Code Search: Incorporating User Feedback to Improve Code Search Relevance
title_full Active Code Search: Incorporating User Feedback to Improve Code Search Relevance
title_fullStr Active Code Search: Incorporating User Feedback to Improve Code Search Relevance
title_full_unstemmed Active Code Search: Incorporating User Feedback to Improve Code Search Relevance
title_sort active code search: incorporating user feedback to improve code search relevance
publisher Institutional Knowledge at Singapore Management University
publishDate 2014
url https://ink.library.smu.edu.sg/sis_research/2424
https://ink.library.smu.edu.sg/context/sis_research/article/3424/viewcontent/p677_wang.pdf
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