SMURF: A SVM-based Incremental Anti-pattern Detection Approach. Maiga, A., Ali, N., Bhattacharya, N., Saban�, A., Gu�h�neuc, Y., Antoniol, G., & Aimeur, E. In Oliveto, R. & Poshyvanyk, D., editors, Proceedings of the 19<sup>th</sup> Working Conference on Reverse Engineering (WCRE), pages 466–475, October, 2012. IEEE CS Press. 10 pages.![SMURF: A SVM-based Incremental Anti-pattern Detection Approach [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper abstract bibtex In current, typical software development projects, hundreds of developers work asynchronously in space and time and may introduce anti-patterns in their software systems because of time pressure, lack of understanding, communication, and–or skills. Anti-patterns impede development and maintenance activities by making the source code more difficult to understand. Detecting anti-patterns incrementally and on subsets of a system could reduce costs, effort, and resources by allowing practitioners to identify and take into account occurrences of anti-patterns as they find them during their development and maintenance activities. Researchers have proposed approaches to detect occurrences of anti-patterns but these approaches have currently four limitations: (1) they require extensive knowledge of anti-patterns, (2) they have limited precision and recall, (3) they are not incremental, and (4) they cannot be applied on subsets of systems. To overcome these limitations, we introduce SMURF, a novel approach to detect anti-patterns, based on a machine learning technique—support vector machines—and taking into account practitioners' feedback. Indeed, through an empirical study involving three systems and four anti-patterns, we showed that the accuracy of SMURF is greater than that of DETEX and BDTEX when detecting anti-patterns occurrences. We also showed that SMURF can be applied in both intra-system and inter-system configurations. Finally, we reported that SMURF accuracy improves when using practitioners' feedback.
@INPROCEEDINGS{Maiga12-WCRE-SMURF,
AUTHOR = {Abdou Maiga and Nasir Ali and Neelesh Bhattacharya and
Aminata Saban� and Yann-Ga�l Gu�h�neuc and Giuliano Antoniol and
Esma Aimeur},
BOOKTITLE = {Proceedings of the 19<sup>th</sup> Working Conference on Reverse Engineering (WCRE)},
TITLE = {SMURF: A SVM-based Incremental Anti-pattern Detection
Approach},
YEAR = {2012},
OPTADDRESS = {},
OPTCROSSREF = {},
EDITOR = {Rocco Oliveto and Denys Poshyvanyk},
MONTH = {October},
NOTE = {10 pages.},
OPTNUMBER = {},
OPTORGANIZATION = {},
PAGES = {466--475},
PUBLISHER = {IEEE CS Press},
OPTSERIES = {},
OPTVOLUME = {},
KEYWORDS = {Topic: <b>Code and design smells</b>,
Venue: <c>WCRE</c>},
URL = {http://www.ptidej.net/publications/documents/WCRE12a.doc.pdf},
PDF = {http://www.ptidej.net/publications/documents/WCRE12a.ppt.pdf},
ABSTRACT = {In current, typical software development projects,
hundreds of developers work asynchronously in space and time and may
introduce anti-patterns in their software systems because of time
pressure, lack of understanding, communication, and--or skills.
Anti-patterns impede development and maintenance activities by making
the source code more difficult to understand. Detecting anti-patterns
incrementally and on subsets of a system could reduce costs, effort,
and resources by allowing practitioners to identify and take into
account occurrences of anti-patterns as they find them during their
development and maintenance activities. Researchers have proposed
approaches to detect occurrences of anti-patterns but these
approaches have currently four limitations: (1) they require
extensive knowledge of anti-patterns, (2) they have limited precision
and recall, (3) they are not incremental, and (4) they cannot be
applied on subsets of systems. To overcome these limitations, we
introduce SMURF, a novel approach to detect anti-patterns, based on a
machine learning technique---support vector machines---and taking
into account practitioners' feedback. Indeed, through an empirical
study involving three systems and four anti-patterns, we showed that
the accuracy of SMURF is greater than that of DETEX and BDTEX when
detecting anti-patterns occurrences. We also showed that SMURF can be
applied in both intra-system and inter-system configurations.
Finally, we reported that SMURF accuracy improves when using
practitioners' feedback.}
}
Downloads: 0
{"_id":"GxjGwzgdAH6ocPcL7","bibbaseid":"maiga-ali-bhattacharya-saban-guhneuc-antoniol-aimeur-smurfasvmbasedincrementalantipatterndetectionapproach-2012","downloads":0,"creationDate":"2018-01-17T20:29:42.389Z","title":"SMURF: A SVM-based Incremental Anti-pattern Detection Approach","author_short":["Maiga, A.","Ali, N.","Bhattacharya, N.","Saban�, A.","Gu�h�neuc, Y.","Antoniol, G.","Aimeur, E."],"year":2012,"bibtype":"inproceedings","biburl":"http://www.yann-gael.gueheneuc.net/Work/Publications/Biblio/complete-bibliography.bib","bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["Abdou"],"propositions":[],"lastnames":["Maiga"],"suffixes":[]},{"firstnames":["Nasir"],"propositions":[],"lastnames":["Ali"],"suffixes":[]},{"firstnames":["Neelesh"],"propositions":[],"lastnames":["Bhattacharya"],"suffixes":[]},{"firstnames":["Aminata"],"propositions":[],"lastnames":["Saban�"],"suffixes":[]},{"firstnames":["Yann-Ga�l"],"propositions":[],"lastnames":["Gu�h�neuc"],"suffixes":[]},{"firstnames":["Giuliano"],"propositions":[],"lastnames":["Antoniol"],"suffixes":[]},{"firstnames":["Esma"],"propositions":[],"lastnames":["Aimeur"],"suffixes":[]}],"booktitle":"Proceedings of the 19<sup>th</sup> Working Conference on Reverse Engineering (WCRE)","title":"SMURF: A SVM-based Incremental Anti-pattern Detection Approach","year":"2012","optaddress":"","optcrossref":"","editor":[{"firstnames":["Rocco"],"propositions":[],"lastnames":["Oliveto"],"suffixes":[]},{"firstnames":["Denys"],"propositions":[],"lastnames":["Poshyvanyk"],"suffixes":[]}],"month":"October","note":"10 pages.","optnumber":"","optorganization":"","pages":"466–475","publisher":"IEEE CS Press","optseries":"","optvolume":"","keywords":"Topic: <b>Code and design smells</b>, Venue: <c>WCRE</c>","url":"http://www.ptidej.net/publications/documents/WCRE12a.doc.pdf","pdf":"http://www.ptidej.net/publications/documents/WCRE12a.ppt.pdf","abstract":"In current, typical software development projects, hundreds of developers work asynchronously in space and time and may introduce anti-patterns in their software systems because of time pressure, lack of understanding, communication, and–or skills. Anti-patterns impede development and maintenance activities by making the source code more difficult to understand. Detecting anti-patterns incrementally and on subsets of a system could reduce costs, effort, and resources by allowing practitioners to identify and take into account occurrences of anti-patterns as they find them during their development and maintenance activities. Researchers have proposed approaches to detect occurrences of anti-patterns but these approaches have currently four limitations: (1) they require extensive knowledge of anti-patterns, (2) they have limited precision and recall, (3) they are not incremental, and (4) they cannot be applied on subsets of systems. To overcome these limitations, we introduce SMURF, a novel approach to detect anti-patterns, based on a machine learning technique—support vector machines—and taking into account practitioners' feedback. Indeed, through an empirical study involving three systems and four anti-patterns, we showed that the accuracy of SMURF is greater than that of DETEX and BDTEX when detecting anti-patterns occurrences. We also showed that SMURF can be applied in both intra-system and inter-system configurations. Finally, we reported that SMURF accuracy improves when using practitioners' feedback.","bibtex":"@INPROCEEDINGS{Maiga12-WCRE-SMURF,\r\n AUTHOR = {Abdou Maiga and Nasir Ali and Neelesh Bhattacharya and \r\n Aminata Saban� and Yann-Ga�l Gu�h�neuc and Giuliano Antoniol and \r\n Esma Aimeur},\r\n BOOKTITLE = {Proceedings of the 19<sup>th</sup> Working Conference on Reverse Engineering (WCRE)},\r\n TITLE = {SMURF: A SVM-based Incremental Anti-pattern Detection \r\n Approach},\r\n YEAR = {2012},\r\n OPTADDRESS = {},\r\n OPTCROSSREF = {},\r\n EDITOR = {Rocco Oliveto and Denys Poshyvanyk},\r\n MONTH = {October},\r\n NOTE = {10 pages.},\r\n OPTNUMBER = {},\r\n OPTORGANIZATION = {},\r\n PAGES = {466--475},\r\n PUBLISHER = {IEEE CS Press},\r\n OPTSERIES = {},\r\n OPTVOLUME = {},\r\n KEYWORDS = {Topic: <b>Code and design smells</b>, \r\n Venue: <c>WCRE</c>},\r\n URL = {http://www.ptidej.net/publications/documents/WCRE12a.doc.pdf},\r\n PDF = {http://www.ptidej.net/publications/documents/WCRE12a.ppt.pdf},\r\n ABSTRACT = {In current, typical software development projects, \r\n hundreds of developers work asynchronously in space and time and may \r\n introduce anti-patterns in their software systems because of time \r\n pressure, lack of understanding, communication, and--or skills. \r\n Anti-patterns impede development and maintenance activities by making \r\n the source code more difficult to understand. Detecting anti-patterns \r\n incrementally and on subsets of a system could reduce costs, effort, \r\n and resources by allowing practitioners to identify and take into \r\n account occurrences of anti-patterns as they find them during their \r\n development and maintenance activities. Researchers have proposed \r\n approaches to detect occurrences of anti-patterns but these \r\n approaches have currently four limitations: (1) they require \r\n extensive knowledge of anti-patterns, (2) they have limited precision \r\n and recall, (3) they are not incremental, and (4) they cannot be \r\n applied on subsets of systems. To overcome these limitations, we \r\n introduce SMURF, a novel approach to detect anti-patterns, based on a \r\n machine learning technique---support vector machines---and taking \r\n into account practitioners' feedback. Indeed, through an empirical \r\n study involving three systems and four anti-patterns, we showed that \r\n the accuracy of SMURF is greater than that of DETEX and BDTEX when \r\n detecting anti-patterns occurrences. We also showed that SMURF can be \r\n applied in both intra-system and inter-system configurations. \r\n Finally, we reported that SMURF accuracy improves when using \r\n practitioners' feedback.}\r\n}\r\n\r\n","author_short":["Maiga, A.","Ali, N.","Bhattacharya, N.","Saban�, A.","Gu�h�neuc, Y.","Antoniol, G.","Aimeur, E."],"editor_short":["Oliveto, R.","Poshyvanyk, D."],"key":"Maiga12-WCRE-SMURF","id":"Maiga12-WCRE-SMURF","bibbaseid":"maiga-ali-bhattacharya-saban-guhneuc-antoniol-aimeur-smurfasvmbasedincrementalantipatterndetectionapproach-2012","role":"author","urls":{"Paper":"http://www.ptidej.net/publications/documents/WCRE12a.doc.pdf"},"keyword":["Topic: <b>Code and design smells</b>","Venue: <c>WCRE</c>"],"metadata":{"authorlinks":{"gu�h�neuc, y":"https://bibbase.org/show?bib=http%3A%2F%2Fwww.yann-gael.gueheneuc.net%2FWork%2FPublications%2FBiblio%2Fcomplete-bibliography.bib&msg=embed","guéhéneuc, y":"https://bibbase.org/show?bib=http://www.yann-gael.gueheneuc.net/Work/BibBase/guehene%20(automatically%20cleaned).bib"}},"downloads":0},"search_terms":["smurf","svm","based","incremental","anti","pattern","detection","approach","maiga","ali","bhattacharya","saban�","gu�h�neuc","antoniol","aimeur"],"keywords":["topic: <b>code and design smells</b>","venue: <c>wcre</c>"],"authorIDs":["AfJhKcg96muyPdu7S","xkviMnkrGBneANvMr"],"dataSources":["Sed98LbBeGaXxenrM","8vn5MSGYWB4fAx9Z4"]}