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Paper Abstract and Keywords
Presentation 2022-07-29 13:25
Fault Localization for RNNs Based on Probabilistic Automata and n-grams
Yuta Ishimoto, Masanari Kondo, Naoyasu Ubayashi, Yasutaka Kamei (Kyushu Univ.) SS2022-10 KBSE2022-20
Abstract (in Japanese) (See Japanese page) 
(in English) If deep learning models misbehave, serious accidents may occur.Previous studies have proposed approaches to overcome such misbehavior by detecting and modifying the responsible faulty parts (e.g., neurons of the network) in deep learning models.However, such approaches are not applicable to deep learning models that have internal states that change dynamically based on the input data, for example, recurrent neural networks (RNNs).
To detect misbehavior RNNs, we propose PAFL, a new fault localization approach for application to RNNs.PAFL enables developers to detect faulty parts even in RNNs by computing suspiciousness scores with fault localization using $n$-grams.Furthermore, by using this suspiciousness score, PAFL can extract data strongly associated with RNN misbehavior.Compared to the random approach, PAFL can extract data that are statistically significantly more strongly associated with misbehavior.Specifically, in 83% of all experimental settings for two difficult datasets (i.e., RTMR and IMDB), PAFL can extract data that is difficult to predict of RNNs than randomly extracted data.Our experimental results show that PAFL is useful as a fault localization method for RNNs.
Keyword (in Japanese) (See Japanese page) 
(in English) deep learning / recurrent neural network / fault localization / probabilistic automaton / n-gram / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 138, SS2022-10, pp. 55-60, July 2022.
Paper # SS2022-10 
Date of Issue 2022-07-21 (SS, KBSE) 
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 SS2022-10 KBSE2022-20

Conference Information
Committee SS IPSJ-SE KBSE  
Conference Date 2022-07-28 - 2022-07-30 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaido-Jichiro-Kaikan (Sapporo) 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SS 
Conference Code 2022-07-SS-SE-KBSE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Fault Localization for RNNs Based on Probabilistic Automata and n-grams 
Sub Title (in English)  
Keyword(1) deep learning  
Keyword(2) recurrent neural network  
Keyword(3) fault localization  
Keyword(4) probabilistic automaton  
Keyword(5) n-gram  
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1st Author's Name Yuta Ishimoto  
1st Author's Affiliation Kyushu University (Kyushu Univ.)
2nd Author's Name Masanari Kondo  
2nd Author's Affiliation Kyushu University (Kyushu Univ.)
3rd Author's Name Naoyasu Ubayashi  
3rd Author's Affiliation Kyushu University (Kyushu Univ.)
4th Author's Name Yasutaka Kamei  
4th Author's Affiliation Kyushu University (Kyushu Univ.)
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Speaker Author-1 
Date Time 2022-07-29 13:25:00 
Presentation Time 25 minutes 
Registration for SS 
Paper # SS2022-10, KBSE2022-20 
Volume (vol) vol.122 
Number (no) no.138(SS), no.139(KBSE) 
Page pp.55-60 
#Pages 6 
Date of Issue 2022-07-21 (SS, KBSE) 

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