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Advances in Fuzzy Systems
Volume 2016, Article ID 1479692, 13 pages
http://dx.doi.org/10.1155/2016/1479692
Research Article

Understanding Open Source Software Evolution Using Fuzzy Data Mining Algorithm for Time Series Data

1Department of Computer Science, Guru Nanak Dev University, Amritsar, India
2Department of Computer Science, I.K.G. Punjab Technical University, Jalandhar, Punjab, India

Received 6 June 2016; Accepted 20 July 2016

Academic Editor: Gözde Ulutagay

Copyright © 2016 Munish Saini et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Source code management systems (such as Concurrent Versions System (CVS), Subversion, and git) record changes to code repositories of open source software projects. This study explores a fuzzy data mining algorithm for time series data to generate the association rules for evaluating the existing trend and regularity in the evolution of open source software project. The idea to choose fuzzy data mining algorithm for time series data is due to the stochastic nature of the open source software development process. Commit activity of an open source project indicates the activeness of its development community. An active development community is a strong contributor to the success of an open source project. Therefore commit activity analysis along with the trend and regularity analysis for commit activity of open source software project acts as an important indicator to the project managers and analyst regarding the evolutionary prospects of the project in the future.