RefactorCoderQA: Multi-Agent Coding Benchmark

• RefactorCoderQA is a benchmark and framework designed to assess realistic coding QA across Software Engineering, Data Science, Machine Learning, and NLP using a triple-agent methodology.
• It decomposes tasks into three agents—GuideLLM, SolverLLM, and JudgeLLM—to provide methodological guidance, solution generation, and evaluative feedback.
• Empirical results demonstrate that its fine-tuned RefactorCoder-MoE model achieves 76.84% accuracy, outperforms key baselines, and enhances interpretability through structured reasoning.

RefactorCoderQA is a dual construct introduced as both a benchmark of real-world coding questions and a cloud–edge, multi-agent LLM framework for solving them. It is built from accepted Stack Overflow question–answer pairs and is designed to evaluate structured problem solving across Software Engineering, Data Science, Machine Learning, and Natural Language Processing. In the associated system architecture, a lightweight edge-side model produces methodological guidance, a stronger cloud model generates the solution, and a judge model evaluates correctness, clarity, and efficiency. In reported experiments, the fine-tuned RefactorCoder-MoE model reaches an overall accuracy of $76.84\%$, while human and automated evaluations both indicate high practical relevance and interpretability (Rahman et al., 12 Sep 2025).

1. Definition, scope, and conceptual positioning

RefactorCoderQA is explicitly framed as both a benchmark dataset and a cloud–edge collaborative architecture. As a benchmark, it targets “realistic development tasks” rather than narrow synthetic coding exercises. As a system, it decomposes coding QA into three coordinated roles: GuideLLM, SolverLLM, and JudgeLLM. This design addresses two gaps identified in the source work: a benchmark gap, because existing code benchmarks often focus on single languages or narrow task types, and a single-LLM prompting gap, because raw prompts often yield incomplete or weakly structured answers (Rahman et al., 12 Sep 2025).

A common misconception is that RefactorCoderQA is a refactoring-only benchmark. The underlying paper instead presents it as a multi-domain coding QA resource spanning Software Engineering, Data Science, Machine Learning, and NLP, with both coding and non-coding answers. Refactoring is one recurring task type within the Software Engineering slice, but not the exclusive focus. This suggests that the “Refactor” element in the name is better understood as part of a broader coding-assistant agenda than as a restriction to program transformation alone (Rahman et al., 12 Sep 2025).

At the representation level, each benchmark instance is modeled as

$x_i = (q_i, d_i, a_i),$

where $q_i$ is the problem statement, $d_i$ is the detailed description and context, and $a_i$ is the accepted answer. The task is to predict

$\hat{a}_i = \mathcal{M}(q_i, d_i),$

with $\mathcal{M}$ denoting the model under evaluation. This formulation is closer to instruction-following and developer QA than to standard code-generation benchmarks built around isolated functions (Rahman et al., 12 Sep 2025).

2. Benchmark construction and data organization

The benchmark is collected from Stack Overflow through the official API and retains only questions with an accepted answer. The initial crawl yields 4,352 questions across four domains and eleven fine-grained topics; after manual filtering and normalization, 2,635 high-quality QA pairs remain. Filtering removes ambiguous or incomplete questions, opinion-based answers, duplicates, and formatting artifacts, and each retained instance is converted into an Instruction–Input–Output triplet consisting of a cleaned title, a detailed description, and the accepted answer (Rahman et al., 12 Sep 2025).

The filtered benchmark is distributed across four major domains. Software Engineering includes Python, C, Java, and JavaScript questions; Data Science includes NumPy, Pandas, Matplotlib, and general data-science topics; Machine Learning includes TensorFlow, PyTorch, Scikit-learn, Cross-validation, and Fine-tuning; NLP includes LLM, Llama, Hugging Face, Multimodal, ChatGPT, and Prompt Engineering topics. Domain totals after filtering are 677 for Software Engineering, 827 for Data Science, 626 for Machine Learning, and 505 for NLP (Rahman et al., 12 Sep 2025).

For model development, the paper also reports a train/validation/test partition totaling 2,613 instances: 2,090 for training, 262 for validation, and 261 for test. The slight discrepancy from 2,635 is attributed to additional filtering or leftover entries. The dataset is also annotated by answer type, distinguishing coding from non-coding responses, which supports later ablation-style comparisons (Rahman et al., 12 Sep 2025).

The benchmark’s central claim to realism lies in three properties: it is derived from authentic developer questions, it spans multiple technical domains, and it contains tasks that often require multi-step reasoning rather than single-function synthesis. This is especially visible in examples involving debugging, library APIs, plotting, hyperparameter tuning, prompt engineering, and code modification within contextual problem descriptions (Rahman et al., 12 Sep 2025).

3. Cloud–edge multi-agent architecture

The RefactorCoderQA architecture separates reasoning, solution generation, and evaluation into three specialized agents.

Component	Deployment	Function
GuideLLM	Edge	Produces a step-by-step methodology without code
SolverLLM	Cloud	Generates the executable solution from the problem and guide
JudgeLLM	Cloud	Scores correctness, clarity, and efficiency and provides feedback

GuideLLM receives the problem statement and detailed description and is instructed to produce a “structured, clear, and actionable methodological outline” while explicitly avoiding final code. SolverLLM then consumes the original problem plus the GuideLLM response and produces the implementation. JudgeLLM receives the problem, the guide, and the generated solution, and assigns 1–5 ratings for accuracy, clarity, and efficiency, together with qualitative feedback (Rahman et al., 12 Sep 2025).

Both GuideLLM and SolverLLM are implemented using RefactorCoder-MoE, while JudgeLLM is built on GPT-4o. The architecture is described as cloud–edge collaborative because the guide stage is intended for lightweight edge deployment, whereas solution generation and evaluation are delegated to the cloud. The workflow is therefore not only algorithmically modular but also deployment-aware (Rahman et al., 12 Sep 2025).

The design imposes a distinctive form of structured reasoning. GuideLLM externalizes a plan before any code is produced, SolverLLM is conditioned on that plan, and JudgeLLM evaluates adherence to the outlined methodology in addition to raw answer quality. The paper notes that outputs deviating from the expected structured reasoning format can be marked incorrect even if they are functionally correct. This indicates that RefactorCoderQA evaluates methodological coherence as a first-class property rather than treating reasoning traces as optional decoration (Rahman et al., 12 Sep 2025).

Latency measurements expose the system-level trade-off. Across domains, GuideLLM requires between 4.946 and 6.425 seconds per query, SolverLLM between 5.336 and 6.220 seconds, and JudgeLLM roughly 2.0–2.8 seconds. The authors explicitly state that GuideLLM plus SolverLLM nearly doubles latency relative to a single-model setup, but present this as the cost of higher task alignment and accuracy (Rahman et al., 12 Sep 2025).

4. Model training, evaluation protocol, and scoring

RefactorCoder-MoE is implemented on top of DeepSeek-Coder-7B using 4-bit QLoRA with PEFT. The fine-tuning configuration includes maximum sequence length $512$, LoRA rank $r=8$, scaling $\alpha=16$, dropout $x_i = (q_i, d_i, a_i),$0, AdamW optimization, learning rate $x_i = (q_i, d_i, a_i),$1, weight decay $x_i = (q_i, d_i, a_i),$2, cosine scheduling, batch size per device $x_i = (q_i, d_i, a_i),$3, gradient accumulation $x_i = (q_i, d_i, a_i),$4, and training on 4 × NVIDIA A100 (40 GB) with mixed precision (Rahman et al., 12 Sep 2025).

Automatic evaluation is reduced to a multiple-choice setting. For each test instance, candidate answers are assembled from the accepted Stack Overflow answer plus outputs from RefactorCoder-MoE and baseline models, and the model must select the correct option. Accuracy is defined as

$x_i = (q_i, d_i, a_i),$5

where $x_i = (q_i, d_i, a_i),$6 is the number of test questions, $x_i = (q_i, d_i, a_i),$7 the selected answer, and $x_i = (q_i, d_i, a_i),$8 the correct answer. Domain-wise reporting covers SEQA, DSQA, MLQA, NLPQA, and OverallQA (Rahman et al., 12 Sep 2025).

Human evaluation complements MCQA accuracy. Three expert annotators assess 100 RefactorCoder-MoE responses, with 25 per domain, using 1–5 Likert scores for accuracy, clarity, and efficiency. JudgeLLM is evaluated against these human scores. Reported overall means are 4.63 / 4.64 / 4.51 for humans and 4.74 / 4.84 / 4.64 for JudgeLLM across the same three axes, with an average difference below 0.2 points. The paper interprets this as strong alignment between automated and human evaluation (Rahman et al., 12 Sep 2025).

This evaluation design makes RefactorCoderQA unusual among code benchmarks. It combines a hard automatic metric for answer selection with a softer model-based evaluator validated against human raters. A plausible implication is that the benchmark is intended not only to measure whether a model can produce a plausible solution, but also whether it can participate in a structured workflow where explanations, plans, and evaluative judgments are all machine-processable (Rahman et al., 12 Sep 2025).

5. Empirical results, interpretability, and system trade-offs

RefactorCoder-MoE achieves 76.84% overall accuracy, with domain-wise scores of 74.68% on SEQA, 77.32% on DSQA, 83.01% on MLQA, and 75.70% on NLPQA. The strongest closed-source baseline, GPT-4o Turbo, reaches 70.23% overall, with smaller open-source baselines ranging roughly from the high teens to about thirty percent overall. The largest advantage appears on NLPQA, where RefactorCoder-MoE attains 75.70% versus 63.93% for GPT-4o (Rahman et al., 12 Sep 2025).

The paper also reports answer-type results: RefactorCoder-MoE reaches 75.2% on coding answers and 71.8% on non-coding answers. This supports the claim that the model is not merely specialized to emitting code blocks, but also benefits explanatory and conceptual responses. Because the benchmark includes both modalities, performance cannot be reduced to conventional pass@k code-generation behavior (Rahman et al., 12 Sep 2025).

Interpretability is tied directly to the three-agent decomposition. GuideLLM produces a human-readable plan, SolverLLM operationalizes it, and JudgeLLM explains strengths and weaknesses while scoring the result. The paper’s example on plotting demonstrates this explicitly: the guide enumerates steps, the solver implements them, and the judge returns both narrative feedback and perfect 5/5 scores. This suggests that interpretability in RefactorCoderQA is procedural rather than post hoc: the reasoning chain is part of the system’s native interface (Rahman et al., 12 Sep 2025).

Failure analysis identifies several recurring weaknesses in baseline-style or weaker answers: overly generic advice, omission of edge cases, oversimplified ML guidance, and outdated APIs. These failure modes are domain-specific and often reflect insufficiently structured reasoning. The architecture is therefore presented as a response to exactly this class of errors: GuideLLM constrains problem decomposition before the final solution is attempted (Rahman et al., 12 Sep 2025).

The system’s limitations are equally explicit. The benchmark is Stack Overflow-centric and therefore inherits community and topic biases; 2,635 examples remain moderate in size relative to pretraining corpora; evaluation relies heavily on JudgeLLM; and the multi-agent pipeline increases latency and cost. Future work proposed in the source paper includes broader domain coverage, stronger GuideLLM and JudgeLLM capabilities, additional evaluation axes such as maintainability and security, and model compression for low-latency edge deployment (Rahman et al., 12 Sep 2025).

6. Relation to automated refactoring and QA research

RefactorCoderQA is not a refactoring benchmark in the narrow sense, yet its structure overlaps strongly with contemporary work on LLM-assisted refactoring, correctness assurance, and human-centered evaluation. In automated Extract Method refactoring for Python, for example, iterative prompting with test-driven feedback—Recursive Criticism and Improvement—raises test pass percentage across all evaluated open-source models, with top systems reaching 0.829 and 0.808 TPP while reducing average LOC per method and cyclomatic complexity; developer studies further report over 70% acceptance for RCI-generated refactorings and show that static metrics can diverge from human judgment (Chand et al., 30 Oct 2025). This provides a close methodological analogue to RefactorCoderQA’s own combination of automatic scoring and human-aligned evaluation.

A broader empirical study on Java refactoring finds that one-shot prompting improves unit-test pass rate by 6.15% and smell reduction by 3.52%, while generating five candidates per input improves pass rate by 28.8 percentage points over pass@1; the same study also shows that LLMs outperform developers on several local smell categories but underperform on architecture-sensitive refactorings (Cordeiro et al., 2024). A plausible implication is that RefactorCoderQA’s structured GuideLLM→SolverLLM→JudgeLLM pipeline belongs to a wider design trend in which prompt engineering, candidate selection, and explicit evaluation loops are used to compensate for the uneven strengths of general-purpose code models.

Reinforcement-learning work on extract-method refactoring pushes this further by aligning code models to compile, contain a detectable refactoring, and improve functional correctness, with CodeT5 increasing from 41/122 to 66/122 tests passed after PPO-based alignment (Palit et al., 2024). This suggests that future RefactorCoderQA variants could extend beyond answer selection and JudgeLLM scoring toward direct optimization for code-centric constraints such as compilation, refactoring detectability, or test outcomes.

The human review perspective is also relevant. An empirical study of OpenStack code review identifies 28 refactoring review criteria grouped into quality, refactoring correctness, objective, testing, integration, and management, and shows that refactoring-related reviews are more effortful than non-refactoring reviews across reviewers, comments, revisions, changed files, and churn (AlOmar et al., 2022). A plausible implication is that JudgeLLM’s current axes—accuracy, clarity, and efficiency—could be expanded toward reviewer-oriented dimensions such as behavior preservation, change decomposition, API compatibility, and test adequacy.

Taken together, these adjacent results position RefactorCoderQA within a broader movement from single-shot code generation toward structured, evaluative, and deployment-aware code assistance. The benchmark itself measures multi-domain coding QA, but its architectural principles—agent specialization, explicit intermediate reasoning, and layered evaluation—align closely with emerging best practices in automated refactoring and software-engineering QA research (Rahman et al., 12 Sep 2025).