A Cooperative Parallel Search-Based Software Engineering Approach for Code-Smells Detection
We propose in this paper to consider code-smells detection as a distributed optimization problem. The idea is that different methods are combined in parallel during the optimization process to find a consensus regarding the detection of code-smells. To this end, we used Parallel Evolutionary algorit...
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| Veröffentlicht in: | IEEE transactions on software engineering 2014-09, Vol.40 (9), p.841-861 |
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| container_title | IEEE transactions on software engineering |
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| creator | Kessentini, Wael Kessentini, Marouane Sahraoui, Houari Bechikh, Slim Ouni, Ali |
| description | We propose in this paper to consider code-smells detection as a distributed optimization problem. The idea is that different methods are combined in parallel during the optimization process to find a consensus regarding the detection of code-smells. To this end, we used Parallel Evolutionary algorithms (P-EA) where many evolutionary algorithms with different adaptations (fitness functions, solution representations, and change operators) are executed, in a parallel cooperative manner, to solve a common goal which is the detection of code-smells. An empirical evaluation to compare the implementation of our cooperative P-EA approach with random search, two single population-based approaches and two code-smells detection techniques that are not based on meta-heuristics search. The statistical analysis of the obtained results provides evidence to support the claim that cooperative P-EA is more efficient and effective than state of the art detection approaches based on a benchmark of nine large open source systems where more than 85 percent of precision and recall scores are obtained on a variety of eight different types of code-smells. |
| doi_str_mv | 10.1109/TSE.2014.2331057 |
| format | Article |
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The statistical analysis of the obtained results provides evidence to support the claim that cooperative P-EA is more efficient and effective than state of the art detection approaches based on a benchmark of nine large open source systems where more than 85 percent of precision and recall scores are obtained on a variety of eight different types of code-smells.</description><identifier>ISSN: 0098-5589</identifier><identifier>EISSN: 1939-3520</identifier><identifier>DOI: 10.1109/TSE.2014.2331057</identifier><identifier>CODEN: IESEDJ</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Benchmarking ; Codes ; Computational modeling ; Detectors ; Evolutionary algorithms ; Evolutionary computation ; Fitness ; Genetic algorithms ; Mathematical problems ; Measurement ; Open source software ; Optimization ; Optimization algorithms ; Parallel processing ; Recall ; Representations ; Searching ; Sociology ; Software engineering ; Statistics ; Studies</subject><ispartof>IEEE transactions on software engineering, 2014-09, Vol.40 (9), p.841-861</ispartof><rights>Copyright IEEE Computer Society Sep 1, 2014</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c390t-851575e4989c06614f7058a6f6dd5b3644fd0db9b756566203d73271b66b461d3</citedby><cites>FETCH-LOGICAL-c390t-851575e4989c06614f7058a6f6dd5b3644fd0db9b756566203d73271b66b461d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6835187$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6835187$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Kessentini, Wael</creatorcontrib><creatorcontrib>Kessentini, Marouane</creatorcontrib><creatorcontrib>Sahraoui, Houari</creatorcontrib><creatorcontrib>Bechikh, Slim</creatorcontrib><creatorcontrib>Ouni, Ali</creatorcontrib><title>A Cooperative Parallel Search-Based Software Engineering Approach for Code-Smells Detection</title><title>IEEE transactions on software engineering</title><addtitle>TSE</addtitle><description>We propose in this paper to consider code-smells detection as a distributed optimization problem. 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The statistical analysis of the obtained results provides evidence to support the claim that cooperative P-EA is more efficient and effective than state of the art detection approaches based on a benchmark of nine large open source systems where more than 85 percent of precision and recall scores are obtained on a variety of eight different types of code-smells.</description><subject>Benchmarking</subject><subject>Codes</subject><subject>Computational modeling</subject><subject>Detectors</subject><subject>Evolutionary algorithms</subject><subject>Evolutionary computation</subject><subject>Fitness</subject><subject>Genetic algorithms</subject><subject>Mathematical problems</subject><subject>Measurement</subject><subject>Open source software</subject><subject>Optimization</subject><subject>Optimization algorithms</subject><subject>Parallel processing</subject><subject>Recall</subject><subject>Representations</subject><subject>Searching</subject><subject>Sociology</subject><subject>Software engineering</subject><subject>Statistics</subject><subject>Studies</subject><issn>0098-5589</issn><issn>1939-3520</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkM9LwzAYhoMoOKd3wUvBi5fOL02TNMc55w8YKHSePIS0_bp1dE1NOsX_3oyJB0_v5XlfXh5CLilMKAV1u8znkwRoOkkYo8DlERlRxVTMeALHZASgspjzTJ2SM-83AAGRfETep9HM2h6dGZpPjF6NM22LbZSjceU6vjMeqyi39fBlHEbzbtV0iK7pVtG075015TqqrQsbFcb5FtvWR_c4YDk0tjsnJ7VpPV785pi8PcyXs6d48fL4PJsu4pIpGOKMUy45pipTJQhB01oCz4yoRVXxgok0rSuoClVILrgQCbBKskTSQogiFbRiY3Jz2A2HPnboB71tfBm-mA7tzmsaOpABkzSg1__Qjd25LrzTlAeMBikQKDhQpbPeO6x175qtcd-agt7b1sG23tvWv7ZD5epQaRDxDxcZ4zST7AcFzXjS</recordid><startdate>20140901</startdate><enddate>20140901</enddate><creator>Kessentini, Wael</creator><creator>Kessentini, Marouane</creator><creator>Sahraoui, Houari</creator><creator>Bechikh, Slim</creator><creator>Ouni, Ali</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><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20140901</creationdate><title>A Cooperative Parallel Search-Based Software Engineering Approach for Code-Smells Detection</title><author>Kessentini, Wael ; 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| subjects | Benchmarking Codes Computational modeling Detectors Evolutionary algorithms Evolutionary computation Fitness Genetic algorithms Mathematical problems Measurement Open source software Optimization Optimization algorithms Parallel processing Recall Representations Searching Sociology Software engineering Statistics Studies |
| title | A Cooperative Parallel Search-Based Software Engineering Approach for Code-Smells Detection |
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