Mining succinct and high-coverage API usage patterns from source code
During software development, a developer often needs to discover specific usage patterns of Application Programming Interface (API) methods. However, these usage patterns are often not well documented. To help developers to get such usage patterns, there are approaches proposed to mine client code o...
Gespeichert in:
Hauptverfasser: | , , , , , |
---|---|
Format: | Tagungsbericht |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 328 |
---|---|
container_issue | |
container_start_page | 319 |
container_title | |
container_volume | |
creator | Jue Wang Yingnong Dang Hongyu Zhang Kai Chen Tao Xie Dongmei Zhang |
description | During software development, a developer often needs to discover specific usage patterns of Application Programming Interface (API) methods. However, these usage patterns are often not well documented. To help developers to get such usage patterns, there are approaches proposed to mine client code of the API methods. However, they lack metrics to measure the quality of the mined usage patterns, and the API usage patterns mined by the existing approaches tend to be many and redundant, posing significant barriers for being practical adoption. To address these issues, in this paper, we propose two quality metrics (succinctness and coverage) for mined usage patterns, and further propose a novel approach called Usage Pattern Miner (UP-Miner) that mines succinct and high-coverage usage patterns of API methods from source code. We have evaluated our approach on a large-scale Microsoft codebase. The results show that our approach is effective and outperforms an existing representative approach MAPO. The user studies conducted with Microsoft developers confirm the usefulness of the proposed approach in practice. |
doi_str_mv | 10.1109/MSR.2013.6624045 |
format | Conference Proceeding |
fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_6624045</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6624045</ieee_id><sourcerecordid>6624045</sourcerecordid><originalsourceid>FETCH-LOGICAL-i283t-3069442b62d6e9c1a39757df6c5ba6358f7d3abe3ad727861d2d63b7e0f49d7e3</originalsourceid><addsrcrecordid>eNotkEtLAzEUhSMqWGv3gpv8gam5uZk8lqXUWmhRfKxLJrnTBuxMmcwI_nsrFg6cb3H4FoexexBTAOEeN-9vUykAp1pLJVR5wSbOWFDaoHSo4ZLdgjLOCVSluGIjCVoUYEt5wyY5p0oAWHeKHbHFJjWp2fE8hJCa0HPfRL5Pu30R2m_q_I747HXFh_xHR9_31DWZ11174LkdukA8tJHu2HXtvzJNzj1mn0-Lj_lzsX5ZruazdZGkxb5AoZ1SstIyanIBPDpTmljrUFZeY2lrE9FXhD4aaayGeBpiZUjUykVDOGYP_95ERNtjlw6--9meX8BfMMNNpw</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Mining succinct and high-coverage API usage patterns from source code</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Jue Wang ; Yingnong Dang ; Hongyu Zhang ; Kai Chen ; Tao Xie ; Dongmei Zhang</creator><creatorcontrib>Jue Wang ; Yingnong Dang ; Hongyu Zhang ; Kai Chen ; Tao Xie ; Dongmei Zhang</creatorcontrib><description>During software development, a developer often needs to discover specific usage patterns of Application Programming Interface (API) methods. However, these usage patterns are often not well documented. To help developers to get such usage patterns, there are approaches proposed to mine client code of the API methods. However, they lack metrics to measure the quality of the mined usage patterns, and the API usage patterns mined by the existing approaches tend to be many and redundant, posing significant barriers for being practical adoption. To address these issues, in this paper, we propose two quality metrics (succinctness and coverage) for mined usage patterns, and further propose a novel approach called Usage Pattern Miner (UP-Miner) that mines succinct and high-coverage usage patterns of API methods from source code. We have evaluated our approach on a large-scale Microsoft codebase. The results show that our approach is effective and outperforms an existing representative approach MAPO. The user studies conducted with Microsoft developers confirm the usefulness of the proposed approach in practice.</description><identifier>ISSN: 2160-1852</identifier><identifier>ISBN: 1479903450</identifier><identifier>ISBN: 9781479903450</identifier><identifier>EISBN: 9781467329361</identifier><identifier>EISBN: 1467329363</identifier><identifier>DOI: 10.1109/MSR.2013.6624045</identifier><language>eng</language><publisher>IEEE</publisher><subject>API usage ; Clustering algorithms ; Context ; Data mining ; Indexes ; Measurement ; mining software repositories ; Probabilistic logic ; Redundancy ; sequence mining ; software reuse ; usage pattern</subject><ispartof>2013 10th Working Conference on Mining Software Repositories (MSR), 2013, p.319-328</ispartof><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6624045$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6624045$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Jue Wang</creatorcontrib><creatorcontrib>Yingnong Dang</creatorcontrib><creatorcontrib>Hongyu Zhang</creatorcontrib><creatorcontrib>Kai Chen</creatorcontrib><creatorcontrib>Tao Xie</creatorcontrib><creatorcontrib>Dongmei Zhang</creatorcontrib><title>Mining succinct and high-coverage API usage patterns from source code</title><title>2013 10th Working Conference on Mining Software Repositories (MSR)</title><addtitle>MSR</addtitle><description>During software development, a developer often needs to discover specific usage patterns of Application Programming Interface (API) methods. However, these usage patterns are often not well documented. To help developers to get such usage patterns, there are approaches proposed to mine client code of the API methods. However, they lack metrics to measure the quality of the mined usage patterns, and the API usage patterns mined by the existing approaches tend to be many and redundant, posing significant barriers for being practical adoption. To address these issues, in this paper, we propose two quality metrics (succinctness and coverage) for mined usage patterns, and further propose a novel approach called Usage Pattern Miner (UP-Miner) that mines succinct and high-coverage usage patterns of API methods from source code. We have evaluated our approach on a large-scale Microsoft codebase. The results show that our approach is effective and outperforms an existing representative approach MAPO. The user studies conducted with Microsoft developers confirm the usefulness of the proposed approach in practice.</description><subject>API usage</subject><subject>Clustering algorithms</subject><subject>Context</subject><subject>Data mining</subject><subject>Indexes</subject><subject>Measurement</subject><subject>mining software repositories</subject><subject>Probabilistic logic</subject><subject>Redundancy</subject><subject>sequence mining</subject><subject>software reuse</subject><subject>usage pattern</subject><issn>2160-1852</issn><isbn>1479903450</isbn><isbn>9781479903450</isbn><isbn>9781467329361</isbn><isbn>1467329363</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2013</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotkEtLAzEUhSMqWGv3gpv8gam5uZk8lqXUWmhRfKxLJrnTBuxMmcwI_nsrFg6cb3H4FoexexBTAOEeN-9vUykAp1pLJVR5wSbOWFDaoHSo4ZLdgjLOCVSluGIjCVoUYEt5wyY5p0oAWHeKHbHFJjWp2fE8hJCa0HPfRL5Pu30R2m_q_I747HXFh_xHR9_31DWZ11174LkdukA8tJHu2HXtvzJNzj1mn0-Lj_lzsX5ZruazdZGkxb5AoZ1SstIyanIBPDpTmljrUFZeY2lrE9FXhD4aaayGeBpiZUjUykVDOGYP_95ERNtjlw6--9meX8BfMMNNpw</recordid><startdate>201305</startdate><enddate>201305</enddate><creator>Jue Wang</creator><creator>Yingnong Dang</creator><creator>Hongyu Zhang</creator><creator>Kai Chen</creator><creator>Tao Xie</creator><creator>Dongmei Zhang</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201305</creationdate><title>Mining succinct and high-coverage API usage patterns from source code</title><author>Jue Wang ; Yingnong Dang ; Hongyu Zhang ; Kai Chen ; Tao Xie ; Dongmei Zhang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i283t-3069442b62d6e9c1a39757df6c5ba6358f7d3abe3ad727861d2d63b7e0f49d7e3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2013</creationdate><topic>API usage</topic><topic>Clustering algorithms</topic><topic>Context</topic><topic>Data mining</topic><topic>Indexes</topic><topic>Measurement</topic><topic>mining software repositories</topic><topic>Probabilistic logic</topic><topic>Redundancy</topic><topic>sequence mining</topic><topic>software reuse</topic><topic>usage pattern</topic><toplevel>online_resources</toplevel><creatorcontrib>Jue Wang</creatorcontrib><creatorcontrib>Yingnong Dang</creatorcontrib><creatorcontrib>Hongyu Zhang</creatorcontrib><creatorcontrib>Kai Chen</creatorcontrib><creatorcontrib>Tao Xie</creatorcontrib><creatorcontrib>Dongmei Zhang</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Jue Wang</au><au>Yingnong Dang</au><au>Hongyu Zhang</au><au>Kai Chen</au><au>Tao Xie</au><au>Dongmei Zhang</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Mining succinct and high-coverage API usage patterns from source code</atitle><btitle>2013 10th Working Conference on Mining Software Repositories (MSR)</btitle><stitle>MSR</stitle><date>2013-05</date><risdate>2013</risdate><spage>319</spage><epage>328</epage><pages>319-328</pages><issn>2160-1852</issn><isbn>1479903450</isbn><isbn>9781479903450</isbn><eisbn>9781467329361</eisbn><eisbn>1467329363</eisbn><abstract>During software development, a developer often needs to discover specific usage patterns of Application Programming Interface (API) methods. However, these usage patterns are often not well documented. To help developers to get such usage patterns, there are approaches proposed to mine client code of the API methods. However, they lack metrics to measure the quality of the mined usage patterns, and the API usage patterns mined by the existing approaches tend to be many and redundant, posing significant barriers for being practical adoption. To address these issues, in this paper, we propose two quality metrics (succinctness and coverage) for mined usage patterns, and further propose a novel approach called Usage Pattern Miner (UP-Miner) that mines succinct and high-coverage usage patterns of API methods from source code. We have evaluated our approach on a large-scale Microsoft codebase. The results show that our approach is effective and outperforms an existing representative approach MAPO. The user studies conducted with Microsoft developers confirm the usefulness of the proposed approach in practice.</abstract><pub>IEEE</pub><doi>10.1109/MSR.2013.6624045</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | ISSN: 2160-1852 |
ispartof | 2013 10th Working Conference on Mining Software Repositories (MSR), 2013, p.319-328 |
issn | 2160-1852 |
language | eng |
recordid | cdi_ieee_primary_6624045 |
source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | API usage Clustering algorithms Context Data mining Indexes Measurement mining software repositories Probabilistic logic Redundancy sequence mining software reuse usage pattern |
title | Mining succinct and high-coverage API usage patterns from source code |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-08T06%3A04%3A42IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Mining%20succinct%20and%20high-coverage%20API%20usage%20patterns%20from%20source%20code&rft.btitle=2013%2010th%20Working%20Conference%20on%20Mining%20Software%20Repositories%20(MSR)&rft.au=Jue%20Wang&rft.date=2013-05&rft.spage=319&rft.epage=328&rft.pages=319-328&rft.issn=2160-1852&rft.isbn=1479903450&rft.isbn_list=9781479903450&rft_id=info:doi/10.1109/MSR.2013.6624045&rft_dat=%3Cieee_6IE%3E6624045%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=9781467329361&rft.eisbn_list=1467329363&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=6624045&rfr_iscdi=true |