Locating matching rules by mining software change log

Jung Te Weng, Ming-Shi Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationProceedings of the 9th Joint Conference on Information Sciences, JCIS 2006
DOIs
Publication statusPublished - 2006 Dec 1
Event9th Joint Conference on Information Sciences, JCIS 2006 - Taiwan, ROC, Taiwan
Duration: 2006 Oct 82006 Oct 11

Publication series

NameProceedings of the 9th Joint Conference on Information Sciences, JCIS 2006
Volume2006

Other

Other9th Joint Conference on Information Sciences, JCIS 2006
CountryTaiwan
CityTaiwan, ROC
Period06-10-0806-10-11

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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