Presentation 2009-11-27
Finding Change impacts on Embedded System Software using Machine Learning
Kenichiro HARA, Akira OSADA, Haruhiko KAIYA, Kenji KAIJIRI,
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Abstract(in English) This paper reports the result of applying information retrieval technology to impact analysis of the embedded software and the result improved by expansion using machine learning. Information retrieval performs support to associate a document with a source code by using keywords and the appearance frequency. We used a keyword appearance pattern for machine learning, and such machine learning technique could improve precision of this impact analysis.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Case Study / Keyword Retrieval / Change Impact Analysis / Traceability / Machine Learning / Decision Tree
Paper # KBSE2009-41
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Conference Information
Committee KBSE
Conference Date 2009/11/19(1days)
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Registration To Knowledge-Based Software Engineering (KBSE)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Finding Change impacts on Embedded System Software using Machine Learning
Sub Title (in English)
Keyword(1) Case Study
Keyword(2) Keyword Retrieval
Keyword(3) Change Impact Analysis
Keyword(4) Traceability
Keyword(5) Machine Learning
Keyword(6) Decision Tree
1st Author's Name Kenichiro HARA
1st Author's Affiliation Faculty of Engineering, Shinshu University()
2nd Author's Name Akira OSADA
2nd Author's Affiliation Faculty of Engineering, Shinshu University
3rd Author's Name Haruhiko KAIYA
3rd Author's Affiliation Faculty of Engineering, Shinshu University
4th Author's Name Kenji KAIJIRI
4th Author's Affiliation Faculty of Engineering, Shinshu University
Date 2009-11-27
Paper # KBSE2009-41
Volume (vol) vol.109
Number (no) 307
Page pp.pp.-
#Pages 6
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