SMURF: A SVM-based Incremental Anti-pattern Detection Approach. Maiga, A., Ali, N., Bhattacharya, N., Sabané, A., Guéhéneuc, 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.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{\'e} and Yann-Ga{\"e}l Gu{\'e}h{\'e}neuc and Giuliano Antoniol and Esma Aimeur},
title = {SMURF: {A} {SVM}-based Incremental Anti-pattern Detection Approach},
booktitle = {Proceedings of the 19<sup>{th}</sup> Working Conference on Reverse Engineering ({WCRE})},
year = {2012},
month = {October},
editor = {Rocco Oliveto and Denys Poshyvanyk},
publisher = {IEEE CS Press},
note = {10 pages.},
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.},
grant = {NSERC DG and CRC on Software Patterns},
keywords = {Code and design smells ; WCRE},
kind = {MISA},
language = {english},
url = {http://www.ptidej.net/publications/documents/WCRE12a.doc.pdf},
pdf = {http://www.ptidej.net/publications/documents/WCRE12a.ppt.pdf},
pages = {466--475}
}
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.","Sabané, A.","Guéhéneuc, Y.","Antoniol, G.","Aimeur, E."],"year":2012,"bibtype":"inproceedings","biburl":"http://www.yann-gael.gueheneuc.net/Work/BibBase/guehene (automatically cleaned).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":["Sabané"],"suffixes":[]},{"firstnames":["Yann-Gaël"],"propositions":[],"lastnames":["Guéhéneuc"],"suffixes":[]},{"firstnames":["Giuliano"],"propositions":[],"lastnames":["Antoniol"],"suffixes":[]},{"firstnames":["Esma"],"propositions":[],"lastnames":["Aimeur"],"suffixes":[]}],"title":"SMURF: A SVM-based Incremental Anti-pattern Detection Approach","booktitle":"Proceedings of the 19<sup>th</sup> Working Conference on Reverse Engineering (WCRE)","year":"2012","month":"October","editor":[{"firstnames":["Rocco"],"propositions":[],"lastnames":["Oliveto"],"suffixes":[]},{"firstnames":["Denys"],"propositions":[],"lastnames":["Poshyvanyk"],"suffixes":[]}],"publisher":"IEEE CS Press","note":"10 pages.","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.","grant":"NSERC DG and CRC on Software Patterns","keywords":"Code and design smells ; WCRE","kind":"MISA","language":"english","url":"http://www.ptidej.net/publications/documents/WCRE12a.doc.pdf","pdf":"http://www.ptidej.net/publications/documents/WCRE12a.ppt.pdf","pages":"466–475","bibtex":"@INPROCEEDINGS{Maiga12-WCRE-SMURF,\n author = {Abdou Maiga and Nasir Ali and Neelesh Bhattacharya and Aminata Saban{\\'e} and Yann-Ga{\\\"e}l Gu{\\'e}h{\\'e}neuc and Giuliano Antoniol and Esma Aimeur},\n title = {SMURF: {A} {SVM}-based Incremental Anti-pattern Detection Approach},\n booktitle = {Proceedings of the 19<sup>{th}</sup> Working Conference on Reverse Engineering ({WCRE})},\n year = {2012},\n month = {October},\n editor = {Rocco Oliveto and Denys Poshyvanyk},\n publisher = {IEEE CS Press},\n note = {10 pages.},\n 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.},\n grant = {NSERC DG and CRC on Software Patterns},\n keywords = {Code and design smells ; WCRE},\n kind = {MISA},\n language = {english},\n url = {http://www.ptidej.net/publications/documents/WCRE12a.doc.pdf},\n pdf = {http://www.ptidej.net/publications/documents/WCRE12a.ppt.pdf},\n pages = {466--475}\n}\n\n","author_short":["Maiga, A.","Ali, N.","Bhattacharya, N.","Sabané, A.","Guéhéneuc, 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":["Code and design smells ; WCRE"],"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,"html":""},"search_terms":["smurf","svm","based","incremental","anti","pattern","detection","approach","maiga","ali","bhattacharya","sabané","guéhéneuc","antoniol","aimeur"],"keywords":["code and design smells ; wcre"],"authorIDs":["AfJhKcg96muyPdu7S","xkviMnkrGBneANvMr"],"dataSources":["Sed98LbBeGaXxenrM","8vn5MSGYWB4fAx9Z4"]}