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Paper Abstract and Keywords
Presentation 2026-03-27 14:25
A Note on Fixed-Weighted Multi-Kernel Regression Approach for Software Reliability Prediction
Jiacheng Bai, Tadashi Dohi, Junjun Zheng, Hiroyuki Okamura (Hiroshima Univ.) R2025-58
Abstract (in Japanese) (See Japanese page) 
(in English) Software reliability is critical for ensuring the quality and stability of software systems. Traditional models, such as non-homogeneous Poisson processes (NHPPs), often rely on strong distributional assumptions and lack flexibility in capturing complex fault-detection trends. In contrast, nonparametric approaches like kernel regression offer greater modeling flexibility. In this note, we focus on two classical estimators; the Nadaraya–Watson (NW) estimator and the Local Linear (LL) estimator, which are widely recognized for their smoothing performance and their ability to reduce boundary bias. However, conventional single-kernel methods exhibit limited adaptability across diverse datasets. To address this limitation, we propose a fixed-weighted multi-kernel extension for both the NW and LL estimators. In this framework, multiple kernels are linearly combined with
pre-specified weights to improve modeling flexibility while keeping the system simple and stable. Under the fixed-weighted multi-kernel setting, we employ least squares cross-validation (LSCV) to select the bandwidths, enabling data-adaptive smoothing without optimizing kernel weights. Experimental results on real-world software fault-count datasets show that the proposed multi-kernel nonparametric estimators consistently improve over their single-kernel counterparts and provide a robust alternative to classical NHPP-based models.
Keyword (in Japanese) (See Japanese page) 
(in English) software reliability / kernel regression / nonparametric regression / multi-kernel regression / fixed weights / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 428, R2025-58, pp. 21-26, March 2026.
Paper # R2025-58 
Date of Issue 2026-03-20 (R) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 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 R2025-58

Conference Information
Committee R  
Conference Date 2026-03-27 - 2026-03-27 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English) Reliability of Electromechanical Devices, Reliability of Computer System, Reliability General 
Paper Information
Registration To R 
Conference Code 2026-03-R 
Language English (Japanese title is available) 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Note on Fixed-Weighted Multi-Kernel Regression Approach for Software Reliability Prediction 
Sub Title (in English)  
Keyword(1) software reliability  
Keyword(2) kernel regression  
Keyword(3) nonparametric regression  
Keyword(4) multi-kernel regression  
Keyword(5) fixed weights  
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1st Author's Name Jiacheng Bai  
1st Author's Affiliation Hiroshima University (Hiroshima Univ.)
2nd Author's Name Tadashi Dohi  
2nd Author's Affiliation Hiroshima University (Hiroshima Univ.)
3rd Author's Name Junjun Zheng  
3rd Author's Affiliation Hiroshima University (Hiroshima Univ.)
4th Author's Name Hiroyuki Okamura  
4th Author's Affiliation Hiroshima University (Hiroshima Univ.)
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Speaker Author-1 
Date Time 2026-03-27 14:25:00 
Presentation Time 25 minutes 
Registration for R 
Paper # R2025-58 
Volume (vol) vol.125 
Number (no) no.428 
Page pp.21-26 
#Pages 6 
Date of Issue 2026-03-20 (R) 

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