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Paper Abstract and Keywords
Presentation 2019-06-14 15:30
Identification comparison of software fault-prone modules using nonlinear logistic regression models
Kazunari Yamanaka, Tadashi Dohi, Hiroyuki Okamura (Hiroshima U.) R2019-12
Abstract (in Japanese) (See Japanese page) 
(in English) In this article, we compare several non-linear logistic regression models used in a fault-prone
identification problem in terms of the predictive performance. For the project data measured
in actual software development projects, we evaluate the F-score defined by the tradeoff
between accuracy and recall, and compare three logistic regression models; linear logistic regression,
semi-definite logistic regression and kernel regressions. Especially, using the kernel method
yields increase of the dimension in the feature vector. Also, since the computation of inner products
can be easily replaced by the computation of positive definite kernel functions, we have an advantage
to execute the complex non-linear computation with higher speed. Finally, it is shown
that the kernel logistic regression models could identify the fault-prone modules more accurately
than the existing logistic regression models.
Keyword (in Japanese) (See Japanese page) 
(in English) software fault prediction / fault-prone module / non-linear logistic regression / identification problem / / kernel method / F-score /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 82, R2019-12, pp. 19-24, June 2019.
Paper # R2019-12 
Date of Issue 2019-06-07 (R) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF R2019-12

Conference Information
Committee R  
Conference Date 2019-06-14 - 2019-06-14 
Place (in Japanese) (See Japanese page) 
Place (in English) Kikai-Shinko-Kaikan Bldg. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Overall reliability engineering 
Paper Information
Registration To R 
Conference Code 2019-06-R 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Identification comparison of software fault-prone modules using nonlinear logistic regression models 
Sub Title (in English)  
Keyword(1) software fault prediction  
Keyword(2) fault-prone module  
Keyword(3) non-linear logistic regression  
Keyword(4) identification problem  
Keyword(6) kernel method  
Keyword(7) F-score  
1st Author's Name Kazunari Yamanaka  
1st Author's Affiliation Hiroshima University (Hiroshima U.)
2nd Author's Name Tadashi Dohi  
2nd Author's Affiliation Hiroshima University (Hiroshima U.)
3rd Author's Name Hiroyuki Okamura  
3rd Author's Affiliation Hiroshima University (Hiroshima U.)
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Speaker Author-2 
Date Time 2019-06-14 15:30:00 
Presentation Time 25 minutes 
Registration for R 
Paper # R2019-12 
Volume (vol) vol.119 
Number (no) no.82 
Page pp.19-24 
Date of Issue 2019-06-07 (R) 

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