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Paper Abstract and Keywords
Presentation 2022-07-29 16:25
Recovering build-reproducible Dockerfile based on Docker image analysis
Ayaka Kinoshita, Takashi Kobayashi (Tokyo Tech.) SS2022-16 KBSE2022-26
Abstract (in Japanese) (See Japanese page) 
(in English) Since the software depends on external libraries and other software, it is necessary to define an execution environment guaranteed to work when distributing it. Container Virtualization, represented by Docker, is used to provide sharable execution environments to reduce the effort to ensure that the software works on various environments. Many OSS projects include Dockerfile which is a definition file for a container image as a required execution environment. Using container images provides high reproducibility of the environment, however, reproducibility of building an image from Dockerfile, which is a definition file for an image, has not been discussed yet. In this report, we propose a method to recover a highly build-reproducible Dockerfile from a container image. We define a metric to measure build reproducibility, and show that Dockerfiles shared on GitHub are insufficient for reproducibility. We also show that technical challenges to recover a Dockerfile from an image using Docker features, and propose a method to generate a highly build-reproducible Dockerfile by analyzing image layers and extracting specific necessary dependencies for building an environment. Through evaluation experiments, we show that the proposed method can generate a Dockerfile with high reproducibility of container image construction.
Keyword (in Japanese) (See Japanese page) 
(in English) Build Reproducibility / Dockerfile / Reverse Engineering / Container Virtualization / Repository Mining / Analyzing Docker Image Layers / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 138, SS2022-16, pp. 91-96, July 2022.
Paper # SS2022-16 
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 SS2022-16 KBSE2022-26

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) Recovering build-reproducible Dockerfile based on Docker image analysis 
Sub Title (in English)  
Keyword(1) Build Reproducibility  
Keyword(2) Dockerfile  
Keyword(3) Reverse Engineering  
Keyword(4) Container Virtualization  
Keyword(5) Repository Mining  
Keyword(6) Analyzing Docker Image Layers  
Keyword(7)  
Keyword(8)  
1st Author's Name Ayaka Kinoshita  
1st Author's Affiliation Tokyo Institute of Technology (Tokyo Tech.)
2nd Author's Name Takashi Kobayashi  
2nd Author's Affiliation Tokyo Institute of Technology (Tokyo Tech.)
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Speaker Author-1 
Date Time 2022-07-29 16:25:00 
Presentation Time 25 minutes 
Registration for SS 
Paper # SS2022-16, KBSE2022-26 
Volume (vol) vol.122 
Number (no) no.138(SS), no.139(KBSE) 
Page pp.91-96 
#Pages 6 
Date of Issue 2022-07-21 (SS, KBSE) 

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