Design pattern mining enhanced by machine learning

Rudolf Ferenc, Árpád Beszédes, Lajos Fülöp, János Lele

Research output: Conference contribution

55 Citations (Scopus)

Abstract

Design patterns present good solutions to frequently occurring problems in object-oriented software design. Thus their correct application in a system's design may significantly improve its internal quality attributes such as reusability and maintainability. In software maintenance the existence of up-to-date documentation is crucial, so the discovery of as yet unknown design pattern instances can help improve the documentation. Hence a reliable design pattern recognition system is very desirable. However, simpler methods (based on pattern matching) may give imprecise results due to the vague nature of the patterns' structural description. In previous work we presented a pattern matching-based system using the Columbus framework with which we were able to find pattern instances from the source code by considering the patterns' structural descriptions only, and therefore we could not identify false hits and distinguish similar design patterns such as State and Strategy. In the present work we use machine learning to enhance pattern mining by filtering out as many false hits as possible. To do so we distinguish true and false pattern instances with the help of a learning database created by manually tagging a large C++ system.

Original languageEnglish
Title of host publicationProceedings of the 21st IEEE International Conference on Software Maintenance, ICSM 2005
Pages295-304
Number of pages10
DOIs
Publication statusPublished - dec. 1 2005
Event21st IEEE International Conference on Software Maintenance, ICSM 2005 - Budapest, Hungary
Duration: szept. 26 2005szept. 29 2005

Publication series

NameIEEE International Conference on Software Maintenance, ICSM
Volume2005

Other

Other21st IEEE International Conference on Software Maintenance, ICSM 2005
CountryHungary
CityBudapest
Period9/26/059/29/05

ASJC Scopus subject areas

  • Software

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  • Cite this

    Ferenc, R., Beszédes, Á., Fülöp, L., & Lele, J. (2005). Design pattern mining enhanced by machine learning. In Proceedings of the 21st IEEE International Conference on Software Maintenance, ICSM 2005 (pp. 295-304). [1510125] (IEEE International Conference on Software Maintenance, ICSM; Vol. 2005). https://doi.org/10.1109/ICSM.2005.40