Support Vector Machines for Anti-pattern Detection. Maiga, A., Ali, N., Bhattacharya, N., Saban�, A., Gu�h�neuc, Y., Antoniol, G., & Aimeur, E. In Menzies, T. & Saeki, M., editors, Proceedings of the 27<sup>th</sup> Conference on Automated Software Engineering (ASE), pages 278–281, September, 2012. ACM Press. Short paper. 4 pages.
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Paper abstract bibtex
Paper abstract bibtex Developers 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 in a whole software system may be infeasible because of the required parsing time and of the subsequent needed manual validation. Detecting anti-patterns on subsets of a system could reduce costs, effort, and resources. Researchers have proposed approaches to detect occurrences of anti-patterns but these approaches have currently some limitations: they require extensive knowledge of anti-patterns, they have limited precision and recall, and they cannot be applied on subsets of systems. To overcome these limitations, we introduce SVMDetect, a novel approach to detect anti-patterns, based on a machine learning technique—support vector machines. Indeed, through an empirical study involving three subject systems and four anti-patterns, we showed that the accuracy of SVMDetect is greater than of DETEX when detecting anti-patterns occurrences on a set of classes. Concerning, the whole system, SVMDetect is able to find more anti-patterns occurrences than DETEX.
@INPROCEEDINGS{Maiga12-ASE-FeedbackAntipatterns,
AUTHOR = {Abddou 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 27<sup>th</sup> Conference on Automated Software Engineering (ASE)},
TITLE = {Support Vector Machines for Anti-pattern Detection},
YEAR = {2012},
OPTADDRESS = {},
OPTCROSSREF = {},
EDITOR = {Tim Menzies and Motoshi Saeki},
MONTH = {September},
NOTE = {Short paper. 4 pages.},
OPTNUMBER = {},
OPTORGANIZATION = {},
PAGES = {278--281},
PUBLISHER = {ACM Press},
OPTSERIES = {},
OPTVOLUME = {},
KEYWORDS = {Topic: <b>Code and design smells</b>, Venue: <c>ASE</c>},
URL = {http://www.ptidej.net/publications/documents/ASE12.doc.pdf},
PDF = {http://www.ptidej.net/publications/documents/ASE12.ppt.pdf},
ABSTRACT = {Developers 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 in a whole software system may be
infeasible because of the required parsing time and of the subsequent
needed manual validation. Detecting anti-patterns on subsets of a
system could reduce costs, effort, and resources. Researchers have
proposed approaches to detect occurrences of anti-patterns but these
approaches have currently some limitations: they require extensive
knowledge of anti-patterns, they have limited precision and recall,
and they cannot be applied on subsets of systems. To overcome these
limitations, we introduce SVMDetect, a novel approach to detect
anti-patterns, based on a machine learning technique---support vector
machines. Indeed, through an empirical study involving three subject
systems and four anti-patterns, we showed that the accuracy of
SVMDetect is greater than of DETEX when detecting anti-patterns
occurrences on a set of classes. Concerning, the whole system,
SVMDetect is able to find more anti-patterns occurrences than DETEX.}
}Downloads: 0
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