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DRIVE: Dockerfile Rule Mining and Violation Detection

Zhou, Yu; Zhan, Weilin; Li, Zi; Han, Tingting; Chen, Taolue; Gall, Harald (2023). DRIVE: Dockerfile Rule Mining and Violation Detection. ACM Transactions on Software Engineering and Methodology, 33(2):1-23.

Abstract

A Dockerfile defines a set of instructions to build Docker images, which can then be instantiated to support containerized applications. Recent studies have revealed a considerable amount of quality issues with Dockerfiles. In this article, we propose a novel approach, Dockerfiles Rule mIning and Violation dEtection (DRIVE), to mine implicit rules and detect potential violations of such rules in Dockerfiles. DRIVE first parses Dockerfiles and transforms them to an intermediate representation. It then leverages an efficient sequential pattern mining algorithm to extract potential patterns. With heuristic-based reduction and moderate human intervention, potential rules are identified, which can then be utilized to detect potential violations of Dockerfiles. DRIVE identifies 34 semantic rules and 19 syntactic rules including 9 new semantic rules that have not been reported elsewhere. Extensive experiments on real-world Dockerfiles demonstrate the efficacy of our approach.

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Uncontrolled Keywords:software engineering, docker configuration
Scope:Discipline-based scholarship (basic research)
Language:English
Date:21 December 2023
Deposited On:04 Jan 2024 11:00
Last Modified:29 Nov 2024 02:39
Publisher:ACM Digital library
ISSN:1049-331X
OA Status:Hybrid
Publisher DOI:https://doi.org/10.1145/3617173
Other Identification Number:merlin-id:24193
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  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)

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