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Paper Abstract and Keywords
Presentation 2021-03-06 13:40
Research for finding faults in Programs using object detection algorithm by CNN-BI system
Kazuhiko Ogawa, Takako Nakatani (OUJ) KBSE2020-45
Abstract (in Japanese) (See Japanese page) 
(in English) In order to predict the location of program faults, we generated images the source code of a faulty program and trained it with a deep learning algorithm applied to object detection to see if We found program fragments where faults may exist.
We found that the program statements that cause of faults have something in common in the appearance of the source code, and that we could find the faults by applying CNN (Convolutional Neural Network), a type of deep learning.
For the object detection algorithm, we used YOLO (You Look Only Ones), a CNN-based real-time object detection algorithm.
In this paper, we attempted to improve the accuracy of inference and to identify the location of faults, which has been a problem in inferring faults.
Our goal is to improve the accuracy by using an object detection algorithm to infer the location of faults, instead of the image recognition method we have used so far.
In order for the learning models to infer the faults in the program, they learn the actual faults as source code fragments.
After training, we use the learning model to reason about the subject's five programs.
In the inference, we used bounding boxes to enclose faults in the source code of the subject's programs, just as an object detection algorithm would detect animals or artifacts.
In our experiments, we verify that our method improves the accuracy of inferring faults in programs and points out the fault compared to previous inference methods using image recognition.
Keyword (in Japanese) (See Japanese page) 
(in English) bug inference / convolutional nural network / image of source code / deep learning / object detection / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 423, KBSE2020-45, pp. 65-70, March 2021.
Paper # KBSE2020-45 
Date of Issue 2021-02-26 (KBSE) 
ISSN 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 KBSE2020-45

Conference Information
Committee KBSE  
Conference Date 2021-03-05 - 2021-03-06 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To KBSE 
Conference Code 2021-03-KBSE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Research for finding faults in Programs using object detection algorithm by CNN-BI system 
Sub Title (in English)  
Keyword(1) bug inference  
Keyword(2) convolutional nural network  
Keyword(3) image of source code  
Keyword(4) deep learning  
Keyword(5) object detection  
1st Author's Name Kazuhiko Ogawa  
1st Author's Affiliation The Open University of Japan (OUJ)
2nd Author's Name Takako Nakatani  
2nd Author's Affiliation The Open University of Japan (OUJ)
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Speaker Author-1 
Date Time 2021-03-06 13:40:00 
Presentation Time 45 minutes 
Registration for KBSE 
Paper # KBSE2020-45 
Volume (vol) vol.120 
Number (no) no.423 
Page pp.65-70 
Date of Issue 2021-02-26 (KBSE) 

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