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General Information
    • ISSN: 2010-3700 (Online)
    • Abbreviated Title: Int. J. Mach. Learn. Comput.
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
    • E-mail: ijmlc@ejournal.net
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.

IJMLC 2012 Vol.2(4): 517-520 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.179

A Comparison of the Efficiency of Applying Association Rule Discovery on Software Archive using Support – Confidence Model and Support – New Confidence Model

Sunchai Pitakchonlasup and Assadaporn Sapsomboon
Abstract—Software archives contain historical information about the development process of a software system. Using Association rule discovery, the development patterns can be extracted from these archives. This information is useful to support software modification activities, as indicated to software developers which modules are usually modified together during software maintenance or evolution. All previous works focused on mining associations with classical interestingness measure, support-confidence, where some disadvantage existed. The new Interestingness Measure, named as support-new confidence, was proposed by Liu et al. to improve the classical method. In this research, we present the comparison of the efficiency of applying association rule discovery on software archive using classical model and Liu et al.'s model. The experiments were conducted on software archive of KMyMoney software, an open source financial software project. The results show that the efficiency of the rules obtained in new model is higher than the rules obtained in classical model in navigation scenario.

Index Terms—Association rule discovery, measures, version control system, software archives interestingness.

Sunchai Pitakchonlasup is with the Appsphere Group Co.,Ltd., Bangkok Thailand. (e-mail:dz.yoez@gmail.com). Assadaporn Sapsomboon is with Business Software Devlopment Program, Faculty of Commerce and Accoutancy, Chulalongkorn University, Bangkok, Thailand. (e-mail:assadaporn@ acc.chula.ac.th).


Cite:Sunchai Pitakchonlasup and Assadaporn Sapsomboon, "A Comparison of the Efficiency of Applying Association Rule Discovery on Software Archive using Support – Confidence Model and Support – New Confidence Model," International Journal of Machine Learning and Computing vol.2, no. 4, pp. 517-520, 2012.

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