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DynaCI: Generating GitHub Actions Workflows via Dynamic Analysis

Javid Ditty, Rahul Chavan, Nimmi Rashinika Weeraddana, Foyzul Hassan · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29890 · cited by 0 works (OpenAlex) · open-access PDF

Korean

이와 같은 연속적인 CI 통합 (CI)는 구축 및 테스트를 통해 자동으로 코드 변경을 검증하는 필수적인 관행입니다. 그러나, CI 작업 흐름을 구성하는 것은 도전이 남아 있지만, 현대 소프트웨어는 종종 복잡한 아키텍처, 수많은 의존성과 다양한 실행 환경에 대한 지원을 갖추고 있습니다. 대부분의 CI 플랫폼은 또한 특정 도메인 특정 작업 흐름을 사용하여 계획된 작업 흐름을 설명하는 사양을 작성해야합니다. 그 결과, CI 작업 흐름은 종종 오류가 발생하고 CI 기능에 익숙하지 않은 사람들에게 특히 유지하기 어려울 수 있습니다. 자동화 된 CI 작업 흐름 생성에 대한 사전 작업은 개발자가 자연 언어, 부분적 작업 흐름 등을 사용하여 계획된 작업 흐름을

Machine translation (Meta M2M100); the English original follows.

Abstract

Continuous Integration (CI) is an essential practice that automatically validates code changes through builds and tests. However, configuring CI workflows remains challenging, as modern software often features complex architectures, numerous dependencies, and support for diverse execution environments. Most CI platforms also require workflows to be written in their particular domain-specific languages. As a result, CI workflows are often error-prone and hard to maintain, especially for those unfamiliar with CI semantics. Prior work on automated CI workflow generation require developers to create specifications that describe their intended workflows using natural language, partial workflows, etc. However, in practice, developers may struggle to articulate requirements and may be unaware of available CI features. To address these limitations, we propose DynaCI, a novel approach that uses runtime information from developers' local environments to generate CI workflows. DynaCI (i) traces the developers' local environment to collect runtime information, (ii) constructs an initial workflow by applying runtime-informed heuristics, and (iii) iteratively refines the workflow via LLM-based Retrieval Augmented Generation (RAG). We evaluate DynaCI on 50 popular Python projects that use GitHub Actions, a popular CI platform, and compare it against the state-of-the-art CI workflow generation approaches: GH-WCOM and LLM4GW. DynaCI outperforms these existing approaches by more than 21.13% in BLEU score and 20.96% in F1 score on average. It also generates workflows without missing functionality in 56% of cases, while requiring fewer manual modifications to repair such exceptions. These results demonstrate that leveraging runtime information substantially improves LLM-based CI workflow generation, and offer a promising direction for broader DevOps configuration automation.

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Topics: Workflow, Computer science, Python (programming language), Software engineering, Software, DevOps

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange