CIgrate: LLMs for CI Configuration Migration

• CIgrate is an LLM-based framework for automating CI configuration migration between Travis CI and GitHub Actions using zero-shot, few-shot, and fine-tuned approaches.
• It replaces rule-based methods like CIMig with context-driven LLM reasoning to address structural differences and handle uncommon configurations.
• The framework integrates automated similarity metrics with developer validation to rigorously assess configuration equivalence and operational quality.

CIgrate is a proposed LLM-based framework for automatically migrating Continuous Integration (CI) configurations between CI services, specifically between Travis CI and GitHub Actions in both directions. It is framed as a study of whether CI configuration migration can be improved beyond rule-based techniques by using LLMs for configuration translation, under zero-shot, few-shot, and fine-tuned settings. The work is a registered report rather than a completed empirical paper, so it specifies the problem, framework, datasets, research questions, evaluation design, and expected contributions, but does not report final quantitative results or completed developer-study outcomes (Hossain et al., 27 Jul 2025).

1. Definition and problem scope

CIgrate addresses the migration of CI configuration files across CI services as projects evolve. The motivating cases include service deprecation, changes in pricing or usage limits, stronger integration with project-hosting platforms, and changes in required service capabilities. Although both Travis CI and GitHub Actions use YAML-based configuration, their schemas and semantics differ substantially: Travis CI emphasizes language directives, matrix expansions, scripts, and deployment steps, whereas GitHub Actions uses workflow files under .github/workflows, explicit jobs, runs-on, steps, triggers, and reusable actions. Migration is therefore difficult not merely because of syntax variation, but because equivalent behavior is often encoded through structurally different constructs, and some features do not have direct counterparts across services, such as addons or sudo (Hossain et al., 27 Jul 2025).

The paper treats this as a software-configuration translation problem in which a source CI specification must be converted into an equivalent target-service specification. The task can be expressed conceptually as

$M: C_s \rightarrow C_t$

where $C_s$ is the source CI configuration, $C_t$ is the target CI configuration, and $M$ is the migration function implemented by prompting or a fine-tuned LLM. The scope is explicitly limited to bidirectional migration between Travis CI and GitHub Actions; the framework is not presented as a universal translator for CircleCI, GitLab CI, Docker Compose, Kubernetes, or other YAML-based ecosystems (Hossain et al., 27 Jul 2025).

This bounded scope is methodologically important. It indicates that CIgrate is designed around a concrete migration pair for which curated aligned examples and a rule-based baseline already exist, rather than around a service-agnostic formal model of CI semantics.

2. Relation to prior automation, especially CIMig

The primary baseline for CIgrate is CIMig, described as the state-of-the-art automated CI migration approach. CIMig is an example-based method that collects aligned examples of Travis CI and GitHub Actions configurations from repositories, mines transformation rules, uses association rule mining and AST matching, and applies the derived rules to translate configurations. CIgrate is positioned against this rule- and example-driven approach by replacing hand-derived or mined mappings with LLM-based generation grounded in pretrained model knowledge and contextual reasoning (Hossain et al., 27 Jul 2025).

The paper identifies several intended distinctions between the two approaches. First, CIMig depends on predefined or mined mappings extracted from prior aligned migration examples, whereas CIgrate relies on the pretrained knowledge and contextual reasoning of LLMs. Second, CIMig may struggle when a configuration does not match learned patterns closely, while CIgrate is intended to better handle unseen, uncommon, or structurally complex configurations. Third, CIgrate is presented as requiring minimal manual effort in zero-shot or few-shot settings, in contrast to the rule induction pipeline required by CIMig. Fourth, the paper specifically highlights complex job matrices and custom environment variables as difficult features for CIMig, and frames CIgrate as a test of whether LLMs can do better on such cases (Hossain et al., 27 Jul 2025).

The paper does not claim that CIgrate already outperforms CIMig. That comparison is itself formalized as a research question. This is a central interpretive constraint: CIgrate is an evaluation program for LLM-mediated CI migration, not yet an empirically validated replacement for rule-based migration.

3. Framework architecture and migration workflow

The CIgrate framework is described as a phased workflow rather than as a formal algorithm with pseudocode. It begins with data sourcing from the curated migration dataset introduced in the CIMig work and focuses on repositories with confirmed Travis/GitHub Actions migration examples. YAML files are then normalized to remove formatting inconsistencies, reduce noise, and enable structured comparison. The preprocessing stage also categorizes CI features such as matrix builds, environment variables, deployment steps, and caching mechanisms. These normalized source configurations are then fed into an LLM, which is prompted to produce the equivalent target configuration under three setups: zero-shot prompting, few-shot prompting, and fine-tuned inference (Hossain et al., 27 Jul 2025).

The generated outputs are evaluated against human-written reference migrations using lexical and code-oriented similarity measures together with target-service validation tools. The paper further proposes human validation by submitting pull requests containing generated configurations to selected projects, or, when repository maintainers do not respond, by enabling the target CI service on forks and observing the resulting execution behavior. This yields a workflow in which automatic generation, syntactic validation, and developer-facing assessment are all first-class components of the study design (Hossain et al., 27 Jul 2025).

A concise summary of the planned configuration space is as follows:

Dimension	Planned choices	Notes
Migration directions	Travis CI $\rightarrow$ GitHub Actions; GitHub Actions $\rightarrow$ Travis CI	Bidirectional only
Prompting modes	Zero-shot; few-shot; fine-tuned	Fine-tune best open-source model from RQ1
Models	Gemma 3 12B; Llama 3.1 8B; Mistral 7B; GPT-4	Open and proprietary mix
Decoding	Top-1 only; deterministic; temperature $=0$	Aligned with CIMig single-output setting
Validation	Cosine Similarity; CrystalBLEU; CI-specific linters	Human validation planned separately

The prompting component remains deliberately underspecified in the registered report. The paper states that a pilot study will be conducted to develop a well-structured and consistent prompt template, and that prompt design will be standardized across models to reduce internal-validity threats. It does not provide the exact zero-shot prompt, the exact few-shot prompt, the number of few-shot examples, the example-selection strategy, or whether chain-of-thought prompting is used. Likewise, the fine-tuning stage is planned but not specified at the level of optimizer, learning rate, epochs, adapter method, batch size, or hardware (Hossain et al., 27 Jul 2025).

This suggests that the framework is presently stronger as an experimental protocol than as a fully disclosed implementation recipe.

4. Data, models, and evaluation methodology

The study uses the CIMig dataset, described as containing curated migration examples between Travis CI and GitHub Actions for Java projects. The paper mentions 13,403 Travis-only projects, 15,888 GitHub Actions-only projects, and 1,252 dual-CI projects used for testing. Ground truth migrations are derived from real-world repository configurations rather than from synthetic or manually fabricated pairs, which is essential for the paper’s comparison against CIMig on the same migration cases (Hossain et al., 27 Jul 2025).

Four research questions structure the evaluation. RQ1 asks how well zero-shot and few-shot prompting of LLMs migrate CI configurations. RQ2 asks to what extent fine-tuning improves migration accuracy. RQ3 asks how CIgrate compares to CIMig in terms of accuracy and effectiveness. RQ4 asks how developers rate the quality and usability of CIgrate’s output. The model set includes Gemma 3 12B, Llama 3.1 8B, Mistral 7B, and GPT-4, chosen to span both open-source and proprietary systems and to cover different cost-performance trade-offs (Hossain et al., 27 Jul 2025).

Automated evaluation uses three classes of signals. Cosine Similarity is used to assess lexical overlap by representing configurations as token vectors. CrystalBLEU is used because it is designed for code-like text and is more robust to identifier changes, field renaming, and field reordering. CI-specific linters for GitHub Actions and Travis CI are used to ensure that generated outputs are not only similar to reference configurations but also syntactically valid in the target ecosystem. Only the first generated output is evaluated, with deterministic decoding and temperature = 0, reflecting the intended practical scenario in which developers inspect a single candidate migration rather than a beam of alternatives (Hossain et al., 27 Jul 2025).

The paper does not define a formal semantics of CI equivalence, a translation algorithm in pseudocode, a learned optimization function, a custom loss, or a ranking function. “Equivalent” is therefore operationalized empirically through similarity to human-written migration counterparts, syntactic validity under service-specific linters, and eventual developer judgment or CI execution outcomes. A plausible implication is that the study treats CI migration as a pragmatic configuration-transformation problem rather than as a formally verified semantics-preservation problem.

5. Developer validation and validity framework

CIgrate’s developer study is organized around pull-request-based validation. The plan is to choose a curated sample of active GitHub repositories using either Travis CI or GitHub Actions, generate the missing counterpart configuration for each repository, and submit a pull request containing the generated file. Outcomes to be monitored include whether the pull request is approved, merged, or closed, along with developer comments and CI execution results. If developers do not respond within around 1–2 months, the protocol is to fork the repository, enable the target CI service, run the translated configuration, and compare behavior against the original CI pipeline (Hossain et al., 27 Jul 2025).

Developers are to be invited to rate the generated configurations on a 5-point Likert scale across three dimensions: accuracy and completeness, readability and maintainability, and willingness to adopt. The planned statistical analysis uses non-parametric tests such as the Wilcoxon signed-rank test. No effect sizes, significance threshold, or correction method are specified in the registered report (Hossain et al., 27 Jul 2025).

The paper explicitly organizes threats to validity into construct, internal, external, and conclusion validity. Construct-validity concerns center on the fact that similarity metrics may not fully measure correctness or practical viability; proposed mitigations include CI-specific linters, manual validation, and developer feedback. Internal-validity concerns include prompt design, model differences, and API inconsistencies; mitigations include prompt standardization, identical decoding settings, normalized inputs, common datasets and metrics, and a shared inference environment for open-source models. External-validity limitations arise from the exclusive focus on Travis CI, GitHub Actions, open-source repositories, and a selected set of LLMs. Conclusion-validity concerns arise from potentially sparse pull-request responses, motivating the use of indirect signals such as merge status, comments, and CI execution outcomes in addition to developer ratings (Hossain et al., 27 Jul 2025).

These design choices indicate that the study treats human acceptance and executable validity as indispensable complements to text-similarity metrics, which is appropriate for CI artifacts whose operational correctness may not be visible from surface similarity alone.

6. Publication status, limitations, and significance

CIgrate is not a completed benchmark paper but a registered report. Accordingly, there are no reported quantitative scores for zero-shot, few-shot, or fine-tuned CIgrate variants; no completed comparisons against CIMig; no statistical significance outcomes; no completed developer ratings; no observed strengths or weaknesses by model; and no empirically established failure modes. The paper’s contribution lies in defining a rigorous comparative methodology for testing whether LLMs can improve automated CI service migration over rule-based techniques, and in articulating the expected contribution of an LLM-powered migration framework for software configuration evolution (Hossain et al., 27 Jul 2025).

The acknowledged limitations are substantial and should be read as part of the subject’s definition. The study is limited to Travis CI and GitHub Actions, and the dataset is restricted to Java projects, which may limit generalizability. Cosine Similarity and CrystalBLEU are recognized as imperfect proxies because they may not capture semantic equivalence, runtime behavior, or practical viability. Developer feedback may be sparse, LLM performance may vary with prompt wording and model version, and enterprise applicability is explicitly left unclear. The paper also suggests future use of Retrieval-Augmented Generation to ground migrations in indexed examples, documentation, and known migration pairs (Hossain et al., 27 Jul 2025).

Within the supplied arXiv material, the exact name “CIgrate” refers to CI service migration rather than to Integrated Gradients variants or graph-explainability methods. In that sense, CIgrate occupies a distinct niche at the intersection of software engineering, configuration translation, and LLM-assisted maintenance. Its present significance is therefore methodological rather than empirical: it formalizes CI configuration migration as a comparative LLM evaluation problem with automated, syntactic, and developer-centered validation, while leaving the substantive question of effectiveness to future reported results.