[Paper] AdaptEval: A Benchmark for Evaluating Large Language Models on Code Snippet Adaptation

Source: arXiv - 2601.04540v1

Overview

The paper introduces AdaptEval, a new benchmark that measures how well large language models (LLMs) can adapt existing code snippets to new requirements—a task that developers perform constantly when reusing code. By grounding the benchmark in real‑world contexts from Stack Overflow and GitHub, the authors provide a practical yardstick for assessing LLMs’ usefulness in everyday software engineering.

Key Contributions

• Real‑world task collection – 1,200+ adaptation tasks harvested from actual developer discussions, preserving the surrounding context (issue description, surrounding code, comments).
• Multi‑granularity annotations – each task includes (a) a high‑level functional requirement and (b) a fine‑grained “adaptation instruction” (e.g., “replace the sorting algorithm with a stable one”).
• Two‑tier evaluation framework –
  1. Adaptation‑level tests that verify the model followed the specific instruction (e.g., changed the right variable, kept the API contract).
  2. Function‑level tests that run the adapted snippet against unit tests to ensure overall correctness.
• Empirical study on six instruction‑tuned LLMs and three reasoning‑oriented LLMs, revealing systematic strengths and weaknesses in code adaptation.
• Open‑source release of the dataset, annotation schema, and evaluation scripts to encourage community adoption.

Methodology

1. Task Mining – The authors scraped Q&A threads from Stack Overflow and pull‑request discussions from GitHub where a developer explicitly asked to modify a piece of code. They filtered for snippets ≤ 50 LOC to keep the adaptation tractable.
2. Annotation Pipeline – Human annotators (software engineers) wrote two layers of specifications:
   • Task‑level: the overall goal (e.g., “add pagination to the API”).
   • Adaptation‑level: the precise change to be made (e.g., “replace the hard‑coded limit with a configurable parameter”).
     They also authored unit tests that capture the intended behavior before and after adaptation.
3. Evaluation Harness – For each model, the benchmark runs two steps:
   • Prompt Generation – The model receives the original snippet, the surrounding context, and the adaptation instruction.
   • Testing – The generated code is first checked for compliance with the adaptation instruction (string‑matching, AST diff) and then executed against the function‑level unit tests in a sandboxed environment.
4. Metrics –
   • Instruction‑Follow Rate (IFR) – proportion of outputs that satisfy the adaptation‑level constraints.
   • Pass@k (k = 1, 5) – standard functional correctness metric used in code generation benchmarks.
   • Composite Score – weighted sum of IFR and Pass@k to reflect both adherence and correctness.

Results & Findings

• Instruction following is the bottleneck – Even the strongest models miss the adaptation instruction in ~22 % of cases, often because they rewrite the whole snippet instead of applying the minimal change.
• Reasoning‑oriented models improve functional correctness when given more complex adaptations (e.g., algorithmic swaps), but they still lag on strict instruction compliance.
• Context matters – Providing the full Stack Overflow thread boosts IFR by ~8 % compared to a stripped‑down prompt, highlighting the value of surrounding discussion.
• Error patterns – Common failures include: (1) forgetting to rename variables, (2) removing required imports, (3) introducing subtle type mismatches that pass unit tests but break downstream integration.

Practical Implications

• Tooling for IDEs – AdaptEval shows that current LLM assistants are good at generating correct code but still need better “patch‑mode” capabilities. IDE plugins could combine an LLM with a lightweight diff‑checker that forces minimal edits, improving developer trust.
• Automated code review – The two‑tier testing framework can be repurposed as a pre‑merge gate: an LLM suggests a change, the adaptation‑level check validates that the reviewer’s intent is respected before functional tests run.
• Continuous integration (CI) pipelines – Teams can integrate AdaptEval’s evaluation harness to benchmark any in‑house LLM or fine‑tuned model before deploying it as a code‑assist service.
• Learning‑by‑example platforms – Because the benchmark preserves real discussion context, it can serve as a curriculum for teaching developers how to phrase adaptation requests to LLMs effectively.

Limitations & Future Work

• Scope of snippets – The benchmark focuses on relatively small, self‑contained functions (≤ 50 LOC). Larger, multi‑file refactorings remain untested.
• Static analysis reliance – Instruction compliance is measured with heuristic AST diffs, which may miss semantic nuances (e.g., changing a constant’s value without a textual diff).
• Model diversity – Only six publicly available LLMs were evaluated; proprietary or domain‑specific models could behave differently.
• Future directions suggested by the authors include expanding to multi‑file adaptation tasks, incorporating dynamic analysis for deeper compliance checks, and exploring reinforcement‑learning‑based fine‑tuning that explicitly rewards minimal, correct edits.

Authors

• Tanghaoran Zhang
• Xinjun Mao
• Shangwen Wang
• Yuxin Zhao
• Yao Lu
• Jin Zhang
• Zhang Zhang
• Kang Yang
• Yue Yu

Paper Information

• arXiv ID: 2601.04540v1
• Categories: cs.SE, cs.AI
• Published: January 8, 2026
• PDF: Download PDF