Evaluating LLMs on Java Code Snippet Adaptation Using a Mutation-Injection Framework

2026-06-30 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, extended

Summary

A new mutation-injection framework is proposed to systematically evaluate large language models (LLMs) on instruction-free Java code snippet adaptation. This framework addresses gaps in existing benchmarks by operating at the code fragment level (3-20 statements), controlling change types via reverse mutation operators, and ensuring scalability. It constructs tasks from open-source Java repositories with at least 70% test coverage and Maven configurations. LLMs will be assessed across three dimensions: identifying the hardest adaptation types (RQ1), scaling performance with adaptation complexity (RQ2), and determining optimal surrounding context (RQ3). Evaluation relies on test-suite re-insertion and fine-grained mutation-level inspection, using models like GPT-4o, Qwen3-Coder, and DeepSeek-R1.

Key takeaway

For AI Scientists or Machine Learning Engineers developing code-generating LLMs, this framework offers a robust method to benchmark instruction-free code adaptation. You should prioritize improving LLM performance on complex, multi-operator adaptations and investigate how context granularity impacts specific change types. This will guide the development of more effective IDE tools for real-world code reuse.

Key insights

A mutation-injection framework enables systematic, instruction-free evaluation of LLMs on Java code snippet adaptation.

Principles

• Mutation injection provides known ground truth.
• Fragment-level adaptation reflects real-world reuse.
• Taxonomy-driven mutations enable scalable studies.

Method

Construct adaptation tasks by applying a taxonomy of reverse mutation operators to real Java code fragments, then evaluate LLM output via test-suite re-insertion and mutation-level inspection.

In practice

• Evaluate LLMs on instruction-free code adaptation.
• Compare LLM performance across context levels.
• Identify hardest adaptation types for LLMs.

Topics

• LLM Evaluation
• Code Adaptation
• Mutation Testing
• Java Programming
• Software Engineering
• Code Generation Benchmarks

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.