AI-Powered Commit Explorer (APCE)

• APCE is a web-based tool that uses a multi-agent LLM pipeline to generate and refine commit messages from GitHub repository data.
• It integrates GitHub with a three-tier architecture (Next.js, Flask, MySQL) to deliver context-aware, high-quality commit messages.
• The evaluation module combines automatic metrics (BLEU, METEOR, ROUGE-L) with structured human ratings to support both practical use and academic research.

Searching arXiv for the named APCE paper and closely related commit-explorer / commit-message research to ground the article. AI-Powered Commit Explorer (APCE) is a web-based tool for generating and evaluating LLM-generated commit messages directly from GitHub repositories. It is designed for two overlapping constituencies: developers who want higher-quality commit messages for practical use, and researchers who want controlled infrastructure for studying prompt design, refinement, and evaluation in commit message generation. Its core design combines GitHub integration, a multi-agent LLM pipeline for generation and refinement, and an evaluation stack that supports both automatic metrics and structured human judgment (Grees et al., 21 Jul 2025).

1. Definition, scope, and scholarly positioning

APCE is introduced as a reusable framework for commit message generation rather than as a new generative model. Its motivation is grounded in the observation that commit messages can be “the only source of information left for future developers” describing what changed and why, while real-world practice often produces incomplete, ambiguous, inconsistent, or rationale-free messages (Grees et al., 21 Jul 2025). In this setting, APCE treats commit message generation as both a software engineering utility and an experimental research problem.

The system has two primary purposes. For practitioners, it enables browsing repositories and commits, selecting a commit, and generating one or more candidate messages that describe what changed and why. For researchers, it provides prompt and approach management, a multi-agent LLM pipeline, automatic metric computation, and human-rating workflows, all backed by persistent data storage (Grees et al., 21 Jul 2025). The paper explicitly positions the contribution as tooling and workflow infrastructure rather than a new model architecture.

A common misconception is to treat APCE as an evaluated benchmark winner in its own right. The paper does not report a completed empirical study with participant statistics, significance tests, or large-scale comparative results. Instead, it presents APCE as a tool demo and as infrastructure for future experiments on LLM-based commit message generation (Grees et al., 21 Jul 2025).

2. Architecture and commit-message generation pipeline

APCE has a three-tier web architecture consisting of a Next.js frontend, a Flask backend, and a MySQL database. It connects to GitHub through Octokit and to LLMs through OpenRouter; the default model is DeepSeek-R1 via OpenRouter, although the model can be changed (Grees et al., 21 Jul 2025). The system is organized around two modules: a Commit Generation Module and an Evaluation Module.

The Commit Generation Module implements a multi-agent prompting pipeline. The first component, the Commit Generation Agent, receives structured repository context such as the diff, pull request title, issue report, commit type, and optionally the original commit message. Each configured “approach” specifies a prompt template, a set of placeholders such as [DIFF], [PR], [IR], [CT], and [OM], and whether refinement is enabled. The second component, the Refinement Agent, evaluates the candidate message against explicit criteria: it must be less than 72 characters, use imperative mood, clearly describe the change, and contain no explanations or reasoning. If the message already satisfies the criteria, the agent returns it unchanged; otherwise it returns a corrected version (Grees et al., 21 Jul 2025).

The default generation prompt instructs the model to generate a high-quality commit message based on code changes, pull request title, issue report, and commit type; to describe both what changed and why; to use imperative mood; and to return only the commit message. APCE then applies deterministic selection logic. If refinement yields an alternative, the system prefers the valid candidate; if both are valid, it prefers a message under 72 characters if only one satisfies that condition, and otherwise prefers the longer valid message. If refinement is disabled, APCE only validates that the response is not an error and does not exceed 200 characters (Grees et al., 21 Jul 2025).

This design separates content production from style enforcement. A plausible implication is that APCE operationalizes prompt engineering as a first-class experimental variable rather than a hidden implementation detail.

3. Evaluation model, metrics, and stored evidence

APCE’s Evaluation Module combines automatic metrics with structured human judgment. Automatic evaluation compares each generated message with the original human-written commit message using BLEU, METEOR, and ROUGE-L. The paper emphasizes that these are standard summarization-style metrics and stores them per approach and per submission, but also notes the broader literature’s caution that lexical-overlap metrics can be misleading when messages differ in wording while preserving meaning (Grees et al., 21 Jul 2025).

Human evaluation is integrated directly into the interface. When “View AI-Generated Messages” is selected for a commit, APCE presents the original message and shuffled AI-generated alternatives with arbitrary indexes to reduce presentation bias. Each generated message is then rated on a 5-point Likert scale across five criteria: Accuracy, Integrity, Readability, Applicability, and Completeness. These dimensions assess whether the message is correct, whether it explains what changed and why, whether it is clear and grammatically sound, whether other developers would plausibly use it, and whether it covers all relevant changes. APCE also stores optional free-text rationales for those ratings (Grees et al., 21 Jul 2025).

The persistent data model is unusually rich for a tool demo. APCE stores submission-level metadata such as commit ID, commit type, original message, pull request title, issue report content, and timestamp; file-level metadata such as filename, status, additions, changes, and deletions; and per-generated-message metadata including the generated text, approach name, success status, whether refinement was used, user ratings, rationale text, and metric scores. All of this is accessible through a password-protected research view (Grees et al., 21 Jul 2025).

A second misconception is to assume that APCE’s evaluation stack has already validated one prompting strategy as superior. The system supports such studies, but the paper presents the measurement framework itself rather than a completed comparative experiment (Grees et al., 21 Jul 2025).

4. Implementation details and research-practice workflows

The frontend is implemented in Next.js, handling consent forms, GitHub token input, commit timeline visualization, generation dialogs, and the research interface. The backend is implemented in Flask and exposes endpoints for GitHub retrieval, LLM invocation through OpenRouter, metric computation, and database persistence. GitHub access uses a personal access token and username supplied at runtime, and the backend relies on Python dependencies for BLEU, METEOR, and ROUGE-L (Grees et al., 21 Jul 2025).

The developer workflow is straightforward. A developer starts APCE, accepts the consent form, enters a GitHub token and username, selects a repository, views the commit timeline, selects a commit, and triggers “View AI Generated Messages.” APCE then runs each configured approach, optionally refines the output, applies its selection logic, and displays the generated messages alongside the original. The paper notes that future versions are expected to separate generation and evaluation more cleanly so that developers can use the generation path without research-oriented study steps (Grees et al., 21 Jul 2025).

The researcher workflow is more elaborate. Researchers configure prompts and approaches in the research view, optionally customize the refinement prompt, recruit participants, and collect ratings and rationales. The stored data then supports quantitative comparison across approaches and qualitative analysis of recurring themes such as overly generic wording or missed key changes (Grees et al., 21 Jul 2025). This makes APCE closer to experimental infrastructure than to a single-purpose coding assistant.

The paper also describes concrete customization points: the OpenRouter API key is configured through .env.local; model choice can be changed by modifying getDeepSeekResponse() in api.js; a different LLM provider can replace OpenRouter through a new response function; and the default consent form can be replaced for other institutional review settings (Grees et al., 21 Jul 2025).

5. Relation to adjacent research on commits, commit-time analysis, and repository memory

APCE is situated within a broader research area that includes retrieval-based commit message generation, issue-to-commit traceability, structured commit knowledge, and repository-specific learning. Within commit message generation proper, the APCE paper places itself after retrieval-based systems such as ChangeScribe, NNGen, and CoRec; generation-oriented systems such as Atom, Xu et al., FIRA, and CCRep; and LLM-based studies such as those of Xue et al. and Zhang et al. (Grees et al., 21 Jul 2025). This establishes APCE as infrastructure for experimenting over those methodological families.

Adjacent work on retrieval-augmented commit message generation strengthens that positioning. “RAG-Enhanced Commit Message Generation” proposes REACT, a framework that retrieves the most relevant diff-message exemplar through a hybrid retriever and then uses that exemplar to guide PLMs and LLMs, improving CodeT5’s BLEU score by up to 55% and Llama 3’s BLEU score by 102% on the CMG task (Zhang et al., 2024). “Optimization is Better than Generation” shifts the problem from generation to optimization, starting from human-written messages and iteratively refining them with LLMs and evaluator feedback, and reports that CMO outperforms state-of-the-art CMG methods and human messages 88.2%–95.4% of the time (Li et al., 16 Jan 2025). These results suggest that APCE’s prompt-managed generation pipeline could naturally be extended toward retrieval-augmented and optimization-based workflows.

Other work broadens the meaning of a commit explorer beyond message generation. “LinkAnchor” treats issue-to-commit link recovery as an autonomous agentic retrieval problem and reports 60%–262% improvements in Hit@1 over prior approaches across case-study projects, indicating that large commit histories can be navigated effectively through lazy-access, tool-based retrieval (Akhavan et al., 17 Aug 2025). “Lore” reinterprets commit messages as structured decision records using Git trailers such as Constraint:, Rejected:, Directive:, Tested:, and Related:, so that commit history becomes queryable institutional knowledge rather than only narrative prose (Stetsenko, 16 Mar 2026). “Semantic Commit” transfers commit-like thinking into natural-language intent management, using knowledge-graph-based RAG for semantic conflict detection and LLM-assisted resolution when new information changes the meaning of existing documents (Vaithilingam et al., 12 Apr 2025). “Learning to Commit” adds repository-specific Online Repository Memory, where an agent attempts historical tasks, compares its patch with the oracle diff, and distills reusable skills about coding style, internal API usage, and architectural invariants (Li et al., 27 Mar 2026).

A further adjacent strand uses APCE in a broader, commit-time sense. In the evaluation of GitHub Copilot Code Review, an APCE is described as an AI system that analyzes code at or around commit or pull-request time to detect issues and assist developers with targeted feedback. That study treats Copilot Code Review as an APCE-like component and finds that, across seven vulnerability datasets, effective recall on critical security vulnerabilities is approximately zero, with comments concentrated on spelling, minor style issues, and small robustness suggestions rather than explicit vulnerability identification (Amro et al., 17 Sep 2025). This suggests that “AI-Powered Commit Explorer” can denote either the specific commit-message tool or a larger design space of AI systems operating on commits and pull requests.

6. Limitations, misconceptions, and likely directions of evolution

The APCE paper names several practical limitations. Large diffs create longer prompts and more complex reasoning burdens, which can slow LLM responses and degrade message quality. GitHub API rate limits can hinder use on repositories with long histories. The current interface is aimed primarily at researchers, so developers encounter consent and evaluation elements that are extraneous in production use. Quality also depends on the chosen LLM, and the paper notes but does not deeply analyze the privacy implications of sending diffs to third-party APIs (Grees et al., 21 Jul 2025).

Planned improvements are concrete rather than speculative. The authors propose asynchronous processing, caching, and queuing to improve responsiveness and reduce redundant API calls; direct import of commit-message datasets for large-scale automated benchmarking; additional evaluation metrics and reporting capabilities; cleaner separation between generation and evaluation modes; and extensibility to other LLM providers and potentially other repository platforms (Grees et al., 21 Jul 2025). These directions preserve the project’s identity as a research platform while making it more usable as a developer-facing tool.

A plausible implication is that future APCE systems will become hybrid rather than purely prompt-centric. Retrieval-augmented generation offers a way to ground commit messages in exemplars (Zhang et al., 2024); optimization-based refinement offers a way to preserve tacit human intent rather than overwrite it (Li et al., 16 Jan 2025); repository-memory approaches offer a way to align outputs with project-specific conventions and historical patterns (Li et al., 27 Mar 2026); and structured commit records such as Lore show how commit history itself can become an executable knowledge source rather than just archival text (Stetsenko, 16 Mar 2026). Under that broader view, APCE is not merely a commit-message generator but a research platform for studying how AI systems read, write, evaluate, and exploit commit history.