Combinatorial Test Generation for Multiple Input Models With Shared Parameters

Combinatorial testing typically considers a single input model and creates a single test set that achieves t t -way coverage. This paper addresses the problem of combinatorial test generation for multiple input models with shared parameters. We formally define the problem and propose an efficient ap...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on software engineering 2022-07, Vol.48 (7), p.2606-2628
Main Authors: Rao, Chang, Li, Nan, Lei, Yu, Guo, Jin, Zhang, Yadong, Kacker, Raghu N., Kuhn, D. Richard
Format: Article
Language:English
Subjects:
Combinatorial analysis
Combinatorial testing
Information science
Mathematical models
multiple input models
Optimization
Parameters
Presses
Redundancy
Schedules
shared parameters
T-way test generation
Test pattern generators
Test sets
Testing
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c333t-4e797dd345389e606e2fc953a7d46caca2c3f8d3421265f60b6f1009a08181003
cites cdi_FETCH-LOGICAL-c333t-4e797dd345389e606e2fc953a7d46caca2c3f8d3421265f60b6f1009a08181003
container_end_page 2628
container_issue 7
container_start_page 2606
container_title IEEE transactions on software engineering
container_volume 48
creator Rao, Chang
Li, Nan
Lei, Yu
Guo, Jin
Zhang, Yadong
Kacker, Raghu N.
Kuhn, D. Richard
description Combinatorial testing typically considers a single input model and creates a single test set that achieves t t -way coverage. This paper addresses the problem of combinatorial test generation for multiple input models with shared parameters. We formally define the problem and propose an efficient approach to generating multiple test sets, one for each input model, that together satisfy t t -way coverage for all of these input models while minimizing the amount of redundancy between these test sets. We report an experimental evaluation that applies our approach to five real-world applications. The results show that our approach can significantly reduce the amount of redundancy between the test sets generated for multiple input models and perform better than a post-optimization approach.
doi_str_mv 10.1109/TSE.2021.3065950
format article
fullrecord <record><control><sourceid>proquest_ieee_</sourceid><recordid>TN_cdi_proquest_journals_2689807712</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9380551</ieee_id><sourcerecordid>2689807712</sourcerecordid><originalsourceid>FETCH-LOGICAL-c333t-4e797dd345389e606e2fc953a7d46caca2c3f8d3421265f60b6f1009a08181003</originalsourceid><addsrcrecordid>eNo9kEFLAzEQhYMoWKt3wUvA89ZJ0mSToxSthVaFVjyGdHeWbtlu1mz24L83pcXTDMx7M28-Qu4ZTBgD87RZv0w4cDYRoKSRcEFGzAiTCcnhkowAjM6k1Oaa3PT9HgBknssReZ_5w7ZuXfShdg3dYB_pHFsMLta-pZUPdDU0se4apIu2GyJd-RKbnn7XcUfXOxewpJ8uuANGDP0tuapc0-PduY7J1-vLZvaWLT_mi9nzMiuEEDGbYm7yshRTKbRBBQp5VRgpXF5OVeEKxwtR6TTnjCtZKdiqiqUXHGimUyPG5PG0twv-Z0ih7d4PoU0nLVfaaMhzxpMKTqoi-L4PWNku1AcXfi0De6RmEzV7pGbP1JLl4WSpEfFfboQGKZn4AwgCZ2U</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2689807712</pqid></control><display><type>article</type><title>Combinatorial Test Generation for Multiple Input Models With Shared Parameters</title><source>IEEE Electronic Library (IEL) Journals</source><creator>Rao, Chang ; Li, Nan ; Lei, Yu ; Guo, Jin ; Zhang, Yadong ; Kacker, Raghu N. ; Kuhn, D. Richard</creator><creatorcontrib>Rao, Chang ; Li, Nan ; Lei, Yu ; Guo, Jin ; Zhang, Yadong ; Kacker, Raghu N. ; Kuhn, D. Richard</creatorcontrib><description><![CDATA[Combinatorial testing typically considers a single input model and creates a single test set that achieves <inline-formula><tex-math notation="LaTeX">t</tex-math> <mml:math><mml:mi>t</mml:mi></mml:math><inline-graphic xlink:href="li-ieq1-3065950.gif"/> </inline-formula>-way coverage. This paper addresses the problem of combinatorial test generation for multiple input models with shared parameters. We formally define the problem and propose an efficient approach to generating multiple test sets, one for each input model, that together satisfy <inline-formula><tex-math notation="LaTeX">t</tex-math> <mml:math><mml:mi>t</mml:mi></mml:math><inline-graphic xlink:href="li-ieq2-3065950.gif"/> </inline-formula>-way coverage for all of these input models while minimizing the amount of redundancy between these test sets. We report an experimental evaluation that applies our approach to five real-world applications. The results show that our approach can significantly reduce the amount of redundancy between the test sets generated for multiple input models and perform better than a post-optimization approach.]]></description><identifier>ISSN: 0098-5589</identifier><identifier>EISSN: 1939-3520</identifier><identifier>DOI: 10.1109/TSE.2021.3065950</identifier><identifier>CODEN: IESEDJ</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Combinatorial analysis ; Combinatorial testing ; Information science ; Mathematical models ; multiple input models ; Optimization ; Parameters ; Presses ; Redundancy ; Schedules ; shared parameters ; T-way test generation ; Test pattern generators ; Test sets ; Testing</subject><ispartof>IEEE transactions on software engineering, 2022-07, Vol.48 (7), p.2606-2628</ispartof><rights>Copyright IEEE Computer Society 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c333t-4e797dd345389e606e2fc953a7d46caca2c3f8d3421265f60b6f1009a08181003</citedby><cites>FETCH-LOGICAL-c333t-4e797dd345389e606e2fc953a7d46caca2c3f8d3421265f60b6f1009a08181003</cites><orcidid>0000-0003-4147-0124 ; 0000-0002-1069-5980 ; 0000-0002-7666-3391 ; 0000-0002-6723-0172 ; 0000-0003-0050-1596</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9380551$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,786,790,27957,27958,55147</link.rule.ids></links><search><creatorcontrib>Rao, Chang</creatorcontrib><creatorcontrib>Li, Nan</creatorcontrib><creatorcontrib>Lei, Yu</creatorcontrib><creatorcontrib>Guo, Jin</creatorcontrib><creatorcontrib>Zhang, Yadong</creatorcontrib><creatorcontrib>Kacker, Raghu N.</creatorcontrib><creatorcontrib>Kuhn, D. Richard</creatorcontrib><title>Combinatorial Test Generation for Multiple Input Models With Shared Parameters</title><title>IEEE transactions on software engineering</title><addtitle>TSE</addtitle><description><![CDATA[Combinatorial testing typically considers a single input model and creates a single test set that achieves <inline-formula><tex-math notation="LaTeX">t</tex-math> <mml:math><mml:mi>t</mml:mi></mml:math><inline-graphic xlink:href="li-ieq1-3065950.gif"/> </inline-formula>-way coverage. This paper addresses the problem of combinatorial test generation for multiple input models with shared parameters. We formally define the problem and propose an efficient approach to generating multiple test sets, one for each input model, that together satisfy <inline-formula><tex-math notation="LaTeX">t</tex-math> <mml:math><mml:mi>t</mml:mi></mml:math><inline-graphic xlink:href="li-ieq2-3065950.gif"/> </inline-formula>-way coverage for all of these input models while minimizing the amount of redundancy between these test sets. We report an experimental evaluation that applies our approach to five real-world applications. The results show that our approach can significantly reduce the amount of redundancy between the test sets generated for multiple input models and perform better than a post-optimization approach.]]></description><subject>Combinatorial analysis</subject><subject>Combinatorial testing</subject><subject>Information science</subject><subject>Mathematical models</subject><subject>multiple input models</subject><subject>Optimization</subject><subject>Parameters</subject><subject>Presses</subject><subject>Redundancy</subject><subject>Schedules</subject><subject>shared parameters</subject><subject>T-way test generation</subject><subject>Test pattern generators</subject><subject>Test sets</subject><subject>Testing</subject><issn>0098-5589</issn><issn>1939-3520</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNo9kEFLAzEQhYMoWKt3wUvA89ZJ0mSToxSthVaFVjyGdHeWbtlu1mz24L83pcXTDMx7M28-Qu4ZTBgD87RZv0w4cDYRoKSRcEFGzAiTCcnhkowAjM6k1Oaa3PT9HgBknssReZ_5w7ZuXfShdg3dYB_pHFsMLta-pZUPdDU0se4apIu2GyJd-RKbnn7XcUfXOxewpJ8uuANGDP0tuapc0-PduY7J1-vLZvaWLT_mi9nzMiuEEDGbYm7yshRTKbRBBQp5VRgpXF5OVeEKxwtR6TTnjCtZKdiqiqUXHGimUyPG5PG0twv-Z0ih7d4PoU0nLVfaaMhzxpMKTqoi-L4PWNku1AcXfi0De6RmEzV7pGbP1JLl4WSpEfFfboQGKZn4AwgCZ2U</recordid><startdate>20220701</startdate><enddate>20220701</enddate><creator>Rao, Chang</creator><creator>Li, Nan</creator><creator>Lei, Yu</creator><creator>Guo, Jin</creator><creator>Zhang, Yadong</creator><creator>Kacker, Raghu N.</creator><creator>Kuhn, D. Richard</creator><general>IEEE</general><general>IEEE Computer Society</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>JQ2</scope><scope>K9.</scope><orcidid>https://orcid.org/0000-0003-4147-0124</orcidid><orcidid>https://orcid.org/0000-0002-1069-5980</orcidid><orcidid>https://orcid.org/0000-0002-7666-3391</orcidid><orcidid>https://orcid.org/0000-0002-6723-0172</orcidid><orcidid>https://orcid.org/0000-0003-0050-1596</orcidid></search><sort><creationdate>20220701</creationdate><title>Combinatorial Test Generation for Multiple Input Models With Shared Parameters</title><author>Rao, Chang ; Li, Nan ; Lei, Yu ; Guo, Jin ; Zhang, Yadong ; Kacker, Raghu N. ; Kuhn, D. Richard</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c333t-4e797dd345389e606e2fc953a7d46caca2c3f8d3421265f60b6f1009a08181003</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Combinatorial analysis</topic><topic>Combinatorial testing</topic><topic>Information science</topic><topic>Mathematical models</topic><topic>multiple input models</topic><topic>Optimization</topic><topic>Parameters</topic><topic>Presses</topic><topic>Redundancy</topic><topic>Schedules</topic><topic>shared parameters</topic><topic>T-way test generation</topic><topic>Test pattern generators</topic><topic>Test sets</topic><topic>Testing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Rao, Chang</creatorcontrib><creatorcontrib>Li, Nan</creatorcontrib><creatorcontrib>Lei, Yu</creatorcontrib><creatorcontrib>Guo, Jin</creatorcontrib><creatorcontrib>Zhang, Yadong</creatorcontrib><creatorcontrib>Kacker, Raghu N.</creatorcontrib><creatorcontrib>Kuhn, D. Richard</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Xplore Digital Library</collection><collection>CrossRef</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><jtitle>IEEE transactions on software engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Rao, Chang</au><au>Li, Nan</au><au>Lei, Yu</au><au>Guo, Jin</au><au>Zhang, Yadong</au><au>Kacker, Raghu N.</au><au>Kuhn, D. Richard</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Combinatorial Test Generation for Multiple Input Models With Shared Parameters</atitle><jtitle>IEEE transactions on software engineering</jtitle><stitle>TSE</stitle><date>2022-07-01</date><risdate>2022</risdate><volume>48</volume><issue>7</issue><spage>2606</spage><epage>2628</epage><pages>2606-2628</pages><issn>0098-5589</issn><eissn>1939-3520</eissn><coden>IESEDJ</coden><abstract><![CDATA[Combinatorial testing typically considers a single input model and creates a single test set that achieves <inline-formula><tex-math notation="LaTeX">t</tex-math> <mml:math><mml:mi>t</mml:mi></mml:math><inline-graphic xlink:href="li-ieq1-3065950.gif"/> </inline-formula>-way coverage. This paper addresses the problem of combinatorial test generation for multiple input models with shared parameters. We formally define the problem and propose an efficient approach to generating multiple test sets, one for each input model, that together satisfy <inline-formula><tex-math notation="LaTeX">t</tex-math> <mml:math><mml:mi>t</mml:mi></mml:math><inline-graphic xlink:href="li-ieq2-3065950.gif"/> </inline-formula>-way coverage for all of these input models while minimizing the amount of redundancy between these test sets. We report an experimental evaluation that applies our approach to five real-world applications. The results show that our approach can significantly reduce the amount of redundancy between the test sets generated for multiple input models and perform better than a post-optimization approach.]]></abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TSE.2021.3065950</doi><tpages>23</tpages><orcidid>https://orcid.org/0000-0003-4147-0124</orcidid><orcidid>https://orcid.org/0000-0002-1069-5980</orcidid><orcidid>https://orcid.org/0000-0002-7666-3391</orcidid><orcidid>https://orcid.org/0000-0002-6723-0172</orcidid><orcidid>https://orcid.org/0000-0003-0050-1596</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0098-5589
ispartof IEEE transactions on software engineering, 2022-07, Vol.48 (7), p.2606-2628
issn 0098-5589
1939-3520
language eng
recordid cdi_proquest_journals_2689807712
source IEEE Electronic Library (IEL) Journals
subjects Combinatorial analysis
Combinatorial testing
Information science
Mathematical models
multiple input models
Optimization
Parameters
Presses
Redundancy
Schedules
shared parameters
T-way test generation
Test pattern generators
Test sets
Testing
title Combinatorial Test Generation for Multiple Input Models With Shared Parameters
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-09-23T01%3A32%3A18IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_ieee_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Combinatorial%20Test%20Generation%20for%20Multiple%20Input%20Models%20With%20Shared%20Parameters&rft.jtitle=IEEE%20transactions%20on%20software%20engineering&rft.au=Rao,%20Chang&rft.date=2022-07-01&rft.volume=48&rft.issue=7&rft.spage=2606&rft.epage=2628&rft.pages=2606-2628&rft.issn=0098-5589&rft.eissn=1939-3520&rft.coden=IESEDJ&rft_id=info:doi/10.1109/TSE.2021.3065950&rft_dat=%3Cproquest_ieee_%3E2689807712%3C/proquest_ieee_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c333t-4e797dd345389e606e2fc953a7d46caca2c3f8d3421265f60b6f1009a08181003%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2689807712&rft_id=info:pmid/&rft_ieee_id=9380551&rfr_iscdi=true