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Paper Abstract and Keywords
Presentation 2021-06-12 15:50
Towards More Accurate Software Bug Prediction: Maximum Likelihood Predictions
Daigo Fujimura, Tadashi Dohi, Hiroyuki Okamura (Hiroshima Univ.) R2021-15
Abstract (in Japanese) (See Japanese page) 
(in English) In this note, we consider point prediction methods for the number of software bugs decetced in
the future with the bug count data experienced in the past, where the underlying software bug-detection
process is described by non-homogeneous Poisson processes. In general, it is known that the
probability distribution of the number of software bugs observed in the past is not identical to one
of the bug counts expected in the future. Nevertheless, the commonly used technique
is to predict the bug counts under a strong assumption that the
probability distribution with model parameters estimated from the past observations holds even
in the future evolution. Since such a plug-in prediction does not work well to guarantee the higher
prediction accuracy, the more accurate prediction of software bug counts is an emerging issue in
software reliability engineering. We focus on the maximum likelihood predictions and propose two
maximum likelihood predictors to predict the future bug-detection processes. Through a numerical
example with an actual software bug count data set, we compare our new prediction methods with the existing
plug-in predictor.
Keyword (in Japanese) (See Japanese page) 
(in English) software reliability model / non-homogeneous Poisson process / predictive performance / plug-in prediction / maximum likelihood prediction / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 64, R2021-15, pp. 25-30, June 2021.
Paper # R2021-15 
Date of Issue 2021-06-05 (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 R2021-15

Conference Information
Committee R  
Conference Date 2021-06-12 - 2021-06-12 
Place (in Japanese) (See Japanese page) 
Place (in English) Online (Zoom) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Reliability General 
Paper Information
Registration To R 
Conference Code 2021-06-R 
Language English (Japanese title is available) 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Towards More Accurate Software Bug Prediction: Maximum Likelihood Predictions 
Sub Title (in English)  
Keyword(1) software reliability model  
Keyword(2) non-homogeneous Poisson process  
Keyword(3) predictive performance  
Keyword(4) plug-in prediction  
Keyword(5) maximum likelihood prediction  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Daigo Fujimura  
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 Hiroyuki Okamura  
3rd Author's Affiliation Hiroshima University (Hiroshima Univ.)
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Speaker Author-1 
Date Time 2021-06-12 15:50:00 
Presentation Time 25 minutes 
Registration for R 
Paper # R2021-15 
Volume (vol) vol.121 
Number (no) no.64 
Page pp.25-30 
#Pages 6 
Date of Issue 2021-06-05 (R) 

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