Atlantis Press Paper Details: Locating Matching Rules by Mining Software Change Log

A software system maintenance activity is typically performed under an environment of lacking knowledge about how to process it. This scarcity of knowledge may be caused by various factors, such as the large size and complexity of the systems, high staff turnover, poor documentation and long-term system maintenance. The study applies Apriori algorithm to extract information from software change logs. Unfortunately, the software change logs generate many rules. Because searches the suitable rule from many rules is difficult and important matter, especially. This study focuses on the software co-change dependency and proposes a classification model based on association mining, to deal with such kind of dependency. The model combines data mining technologies, the traditional decision-tree and neural learning capabilities, to handle the complicated and real cases, and then improve the rule searching efficiency and the matching accuracy.

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title:		Locating Matching Rules by Mining Software Change Log
publication:		JCIS-2006 Proceedings
part of series:		Advances in Intelligent Systems Research
ISBN:		978-90-78677-01-7
ISSN:		1951-6851
DOI:		doi:10.2991/jcis.2006.303 (how to use a DOI)
author(s):		Ming-Shi Wang, Jung-te Weng
publication date:		October 2006
keywords:		Software maintenance, Data mining, Neural Network
abstract:		A software system maintenance activity is typically performed under an environment of lacking knowledge about how to process it. This scarcity of knowledge may be caused by various factors, such as the large size and complexity of the systems, high staff turnover, poor documentation and long-term system maintenance. The study applies Apriori algorithm to extract information from software change logs. Unfortunately, the software change logs generate many rules. Because searches the suitable rule from many rules is difficult and important matter, especially. This study focuses on the software co-change dependency and proposes a classification model based on association mining, to deal with such kind of dependency. The model combines data mining technologies, the traditional decision-tree and neural learning capabilities, to handle the complicated and real cases, and then improve the rule searching efficiency and the matching accuracy.
copyright:		© Atlantis Press. This article is distributed under the terms of the Creative Commons Attribution License, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited.
full text:		JCIS06-FTT-88.pdf (202 K)