🤖 AI Summary
Traditional code coverage metrics and test generation techniques fail for Dockerfiles due to their lack of explicit control-flow constructs, rendering syntactic analysis insufficient for meaningful testing.
Method: This paper proposes the first image-layer–driven automated test generation method for Dockerfiles. Instead of relying on syntactic structure, it defines semantic test objectives based on the actual post-build state of Docker image layers—such as file existence, permissions, and content. The approach integrates Dockerfile instruction parsing, static layer analysis, and target file identification to guide a heuristic search for validating test cases.
Contribution/Results: Evaluated on real-world projects, the generated tests reproduce over 80% of developers’ manually written tests, significantly enhancing the maintainability and reliability verification of Dockerfiles. By grounding test objectives in observable layer semantics rather than abstract syntax, the method bridges a critical gap in containerized application testing and provides a principled foundation for Dockerfile validation.
📝 Abstract
Docker has gained attention as a lightweight container-based virtualization platform. The process for building a Docker image is defined in a text file called a Dockerfile. A Dockerfile can be considered as a kind of source code that contains instructions on how to build a Docker image. Its behavior should be verified through testing, as is done for source code in a general programming language. For source code in languages such as Java, search-based test generation techniques have been proposed. However, existing automated test generation techniques cannot be applied to Dockerfiles. Since a Dockerfile does not contain branches, the coverage metric, typically used as an objective function in existing methods, becomes meaningless. In this study, we propose an automated test generation method for Dockerfiles based on processing results rather than processing steps. The proposed method determines which files should be tested and generates the corresponding tests based on an analysis of Dockerfile instructions and Docker image layers. The experimental results show that the proposed method can reproduce over 80% of the tests created by developers.