Detecting Duplicate Actions in a KDT Script Based on LRS and LCS
In keyword-driven testing (KDT), having duplicate actions in the test script is perhaps the most common bad smell. Once the target user interface is changed, a KDT script with duplicate actions can be difficult to maintain. Thus, detecting and removing duplicate actions is an important task. However...
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Veröffentlicht in: | Journal of Information Science and Engineering 2020-05, Vol.36 (3), p.513-533 |
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creator | 劉建宏(CHIEN-HUNG LIU) 陳偉凱(WOEI-KAE CHEN) 廖振諺(CHEN-YAN LIAO) |
description | In keyword-driven testing (KDT), having duplicate actions in the test script is perhaps the most common bad smell. Once the target user interface is changed, a KDT script with duplicate actions can be difficult to maintain. Thus, detecting and removing duplicate actions is an important task. However, so far, no KDT testing tools support automated duplicate detection. This paper proposes a method and tool, called DDT (Duplicate script Detection Tool), for the tester to quickly identify duplicate actions. Two detection algorithms based on Longest Repeated Substring (LRS) and Longest Common Subsequence (LCS) are presented. In addition, DDT provides a keyword extraction feature that can automatically remove duplicate actions. Our experimental results show that there are 21-42% of duplicate actions in a typical KDT script, DDT can detect these duplicate actions in 3-5 seconds, and up to 58-81% of these duplicate actions should be refactored. |
doi_str_mv | 10.6688/JISE.202005_36(3).0003 |
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Once the target user interface is changed, a KDT script with duplicate actions can be difficult to maintain. Thus, detecting and removing duplicate actions is an important task. However, so far, no KDT testing tools support automated duplicate detection. This paper proposes a method and tool, called DDT (Duplicate script Detection Tool), for the tester to quickly identify duplicate actions. Two detection algorithms based on Longest Repeated Substring (LRS) and Longest Common Subsequence (LCS) are presented. In addition, DDT provides a keyword extraction feature that can automatically remove duplicate actions. Our experimental results show that there are 21-42% of duplicate actions in a typical KDT script, DDT can detect these duplicate actions in 3-5 seconds, and up to 58-81% of these duplicate actions should be refactored.</description><identifier>ISSN: 1016-2364</identifier><identifier>DOI: 10.6688/JISE.202005_36(3).0003</identifier><language>eng</language><publisher>Taipei: 社團法人中華民國計算語言學學會</publisher><subject>Algorithms ; Feature extraction ; Information retrieval ; Reproduction (copying) ; Smell</subject><ispartof>Journal of Information Science and Engineering, 2020-05, Vol.36 (3), p.513-533</ispartof><rights>Copyright Institute of Information Science, Academia Sinica May 2020</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>劉建宏(CHIEN-HUNG LIU)</creatorcontrib><creatorcontrib>陳偉凱(WOEI-KAE CHEN)</creatorcontrib><creatorcontrib>廖振諺(CHEN-YAN LIAO)</creatorcontrib><title>Detecting Duplicate Actions in a KDT Script Based on LRS and LCS</title><title>Journal of Information Science and Engineering</title><description>In keyword-driven testing (KDT), having duplicate actions in the test script is perhaps the most common bad smell. Once the target user interface is changed, a KDT script with duplicate actions can be difficult to maintain. Thus, detecting and removing duplicate actions is an important task. However, so far, no KDT testing tools support automated duplicate detection. This paper proposes a method and tool, called DDT (Duplicate script Detection Tool), for the tester to quickly identify duplicate actions. Two detection algorithms based on Longest Repeated Substring (LRS) and Longest Common Subsequence (LCS) are presented. In addition, DDT provides a keyword extraction feature that can automatically remove duplicate actions. Our experimental results show that there are 21-42% of duplicate actions in a typical KDT script, DDT can detect these duplicate actions in 3-5 seconds, and up to 58-81% of these duplicate actions should be refactored.</description><subject>Algorithms</subject><subject>Feature extraction</subject><subject>Information retrieval</subject><subject>Reproduction (copying)</subject><subject>Smell</subject><issn>1016-2364</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNpVkFFLwzAUhfOg4Jz-BQn4og-tN7lJmr452-mmBcHO55K2qWSMdrbp_7dzA_HpcA8f93AOITcMQqW0fnhd58uQAweQBao7vA8BAM_IjAFTAUclLsjlMGwBuJJCzMhjar2tvGu_aDrud64y3tLFZHTtQF1LDX1LNzSverf39MkMtqZdS7OPnJq2plmSX5HzxuwGe33SOfl8Xm6SVZC9v6yTRRYYLrQPKoMRcobWloLVDcrYSKkbpU2jIIoRSwUoSh3pmtdQA6qGx5KXiEqWwDTOye3x777vvkc7-GLbjX07RRZcAOMcYiYmanWkjOudd3_Mof-hfnEa51cEU9M87P8hGRYSEX8A11tapQ</recordid><startdate>20200501</startdate><enddate>20200501</enddate><creator>劉建宏(CHIEN-HUNG LIU)</creator><creator>陳偉凱(WOEI-KAE CHEN)</creator><creator>廖振諺(CHEN-YAN LIAO)</creator><general>社團法人中華民國計算語言學學會</general><general>Institute of Information Science, Academia Sinica</general><scope>188</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20200501</creationdate><title>Detecting Duplicate Actions in a KDT Script Based on LRS and LCS</title><author>劉建宏(CHIEN-HUNG LIU) ; 陳偉凱(WOEI-KAE CHEN) ; 廖振諺(CHEN-YAN LIAO)</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a248t-ca373213eeb41df359a558f68af607933b6034b878d2d0d036f2952b3365b0183</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Feature extraction</topic><topic>Information retrieval</topic><topic>Reproduction (copying)</topic><topic>Smell</topic><toplevel>online_resources</toplevel><creatorcontrib>劉建宏(CHIEN-HUNG LIU)</creatorcontrib><creatorcontrib>陳偉凱(WOEI-KAE CHEN)</creatorcontrib><creatorcontrib>廖振諺(CHEN-YAN LIAO)</creatorcontrib><collection>Airiti Library</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Journal of Information Science and Engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>劉建宏(CHIEN-HUNG LIU)</au><au>陳偉凱(WOEI-KAE CHEN)</au><au>廖振諺(CHEN-YAN LIAO)</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Detecting Duplicate Actions in a KDT Script Based on LRS and LCS</atitle><jtitle>Journal of Information Science and Engineering</jtitle><date>2020-05-01</date><risdate>2020</risdate><volume>36</volume><issue>3</issue><spage>513</spage><epage>533</epage><pages>513-533</pages><issn>1016-2364</issn><abstract>In keyword-driven testing (KDT), having duplicate actions in the test script is perhaps the most common bad smell. Once the target user interface is changed, a KDT script with duplicate actions can be difficult to maintain. Thus, detecting and removing duplicate actions is an important task. However, so far, no KDT testing tools support automated duplicate detection. This paper proposes a method and tool, called DDT (Duplicate script Detection Tool), for the tester to quickly identify duplicate actions. Two detection algorithms based on Longest Repeated Substring (LRS) and Longest Common Subsequence (LCS) are presented. In addition, DDT provides a keyword extraction feature that can automatically remove duplicate actions. Our experimental results show that there are 21-42% of duplicate actions in a typical KDT script, DDT can detect these duplicate actions in 3-5 seconds, and up to 58-81% of these duplicate actions should be refactored.</abstract><cop>Taipei</cop><pub>社團法人中華民國計算語言學學會</pub><doi>10.6688/JISE.202005_36(3).0003</doi><tpages>21</tpages></addata></record> |
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title | Detecting Duplicate Actions in a KDT Script Based on LRS and LCS |
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