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Contribution Details
| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | DRIVE: Dockerfile Rule Mining and Violation Detection |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Journal Title | ACM Transactions on Software Engineering and Methodology |
| Publisher | ACM Digital library |
| Geographical Reach | international |
| ISSN | 1049-331X |
| Volume | 33 |
| Number | 2 |
| Page Range | 1 - 23 |
| Date | 2023 |
| Abstract Text | 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. |
| Digital Object Identifier | 10.1145/3617173 |
| Other Identification Number | merlin-id:24193 |
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| Keywords | software engineering, docker configuration |