Support Vector Machines for Anti-pattern Detection. Maiga, A., Ali, N., Bhattacharya, N., Sabané, A., Guéhéneuc, 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.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{\'e} and Yann-Ga{\"e}l Gu{\'e}h{\'e}neuc and Giuliano Antoniol and Esma Aimeur},
title = {Support Vector Machines for Anti-pattern Detection},
booktitle = {Proceedings of the 27<sup>{th}</sup> Conference on Automated Software Engineering ({ASE})},
year = {2012},
month = {September},
editor = {Tim Menzies and Motoshi Saeki},
publisher = {ACM Press},
note = {Short paper. 4 pages.},
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.},
grant = {NSERC DG and CRC on Software Patterns},
keywords = {Code and design smells ; ASE},
kind = {MISA},
language = {english},
url = {http://www.ptidej.net/publications/documents/ASE12.doc.pdf},
pdf = {http://www.ptidej.net/publications/documents/ASE12.ppt.pdf},
pages = {278--281},
comment = {Short paper.}
}
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