Automated Library Recommendation

Many third party libraries are available to be downloaded and used. Using such libraries can reduce development time and make the developed software more reliable. However, developers are often unaware of suitable libraries to be used for their projects and thus they miss out on these benefits. To h...

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Main Authors: THUNG, Ferdian, LO, David, LAWALL, Julia
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2013
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Online Access:https://ink.library.smu.edu.sg/sis_research/2026
https://ink.library.smu.edu.sg/context/sis_research/article/3025/viewcontent/wcre13_librecommend.pdf
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Institution: Singapore Management University
Language: English
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spelling sg-smu-ink.sis_research-30252018-12-07T06:33:25Z Automated Library Recommendation THUNG, Ferdian LO, David LAWALL, Julia Many third party libraries are available to be downloaded and used. Using such libraries can reduce development time and make the developed software more reliable. However, developers are often unaware of suitable libraries to be used for their projects and thus they miss out on these benefits. To help developers better take advantage of the available libraries, we propose a new technique that automatically recommends libraries to developers. Our technique takes as input the set of libraries that an application currently uses, and recommends other libraries that are likely to be relevant. We follow a hybrid approach that combines association rule mining and collaborative filtering. The association rule mining component recommends libraries based on a set of library usage patterns. The collaborative filtering component recommends libraries based on those that are used by other similar projects. We investigate the effectiveness of our hybrid approach on 500 software projects that use many third-party libraries. Our experiments show that our approach can recommend libraries with recall rate@5 of 0.852 and recall rate@10 of 0.894. 2013-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2026 info:doi/10.1109/WCRE.2013.6671293 https://ink.library.smu.edu.sg/context/sis_research/article/3025/viewcontent/wcre13_librecommend.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 collaborative filtering data mining software reliability Library and Information Science Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic collaborative filtering
data mining
software reliability
Library and Information Science
Software Engineering
spellingShingle collaborative filtering
data mining
software reliability
Library and Information Science
Software Engineering
THUNG, Ferdian
LO, David
LAWALL, Julia
Automated Library Recommendation
description Many third party libraries are available to be downloaded and used. Using such libraries can reduce development time and make the developed software more reliable. However, developers are often unaware of suitable libraries to be used for their projects and thus they miss out on these benefits. To help developers better take advantage of the available libraries, we propose a new technique that automatically recommends libraries to developers. Our technique takes as input the set of libraries that an application currently uses, and recommends other libraries that are likely to be relevant. We follow a hybrid approach that combines association rule mining and collaborative filtering. The association rule mining component recommends libraries based on a set of library usage patterns. The collaborative filtering component recommends libraries based on those that are used by other similar projects. We investigate the effectiveness of our hybrid approach on 500 software projects that use many third-party libraries. Our experiments show that our approach can recommend libraries with recall rate@5 of 0.852 and recall rate@10 of 0.894.
format text
author THUNG, Ferdian
LO, David
LAWALL, Julia
author_facet THUNG, Ferdian
LO, David
LAWALL, Julia
author_sort THUNG, Ferdian
title Automated Library Recommendation
title_short Automated Library Recommendation
title_full Automated Library Recommendation
title_fullStr Automated Library Recommendation
title_full_unstemmed Automated Library Recommendation
title_sort automated library recommendation
publisher Institutional Knowledge at Singapore Management University
publishDate 2013
url https://ink.library.smu.edu.sg/sis_research/2026
https://ink.library.smu.edu.sg/context/sis_research/article/3025/viewcontent/wcre13_librecommend.pdf
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