PROBE: Benchmarking Code Generation in Large Language Models

2026-07-16 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Data Science & Analytics · Depth: Expert, extended

Summary

PROBE is an extensible benchmark framework designed to systematically evaluate Large Language Models (LLMs) in automated code generation, addressing limitations of existing benchmarks that often target single languages or rely solely on unit test outcomes. It assesses LLM-generated code across three dimensions: functional correctness, proximity to valid solutions, and overall code quality. The framework was used to evaluate six models, including open-source (Qwen2.5:14b, Qwen2.5-Coder:14b, Qwen2.5-Coder:7b, Deepseek-Coder-v2:16b) and proprietary (GPT-4.1-mini, Gemini-2.0-flash) systems, across five programming languages (Python, C++, Java, C, Rust) and three prompting strategies. Findings indicate that while larger models achieve higher pass@k values (none exceeding 0.7), they struggle with harder problems and low-resource languages like Rust. Feedback-incorporation consistently improved functional correctness (+0.05 pass@k), unlike in-context learning, which had negligible impact. Models frequently produce unreliable code with fundamental errors such as missing imports, integer overflows, out-of-bounds accesses, and inefficient algorithms.

Key takeaway

For Machine Learning Engineers and AI Architects deploying LLMs for code generation, you should prioritize robust benchmarking frameworks like PROBE that assess functional correctness, solution proximity, and code quality. Do not over-rely on LLM-generated code without rigorous validation, as models frequently produce fundamental errors and struggle with complex problems. Incorporate compiler feedback mechanisms into your workflows to improve code reliability, especially for compiled languages.

Key insights

Comprehensive LLM code generation benchmarks require diverse metrics beyond unit tests to reveal critical limitations.

Principles

• Benchmarking requires diverse metrics: functional correctness, solution proximity, and code quality.
• Compiler feedback significantly improves LLM code correctness, especially for syntax errors.
• LLM code generation performance degrades sharply with increasing problem difficulty.

Method

PROBE evaluates LLM code generation using a structured framework with diverse metrics, representative workloads from IBM CodeNet, varied prompt templates, and a robust experimental procedure.

In practice

• Implement compiler feedback loops to enhance LLM code generation reliability.
• Prioritize Python, C++, and Java for LLM code generation tasks over C and Rust.
• Validate LLM-generated code for common errors like overflows and inefficient algorithms.

Topics

• LLM Benchmarking
• Code Generation
• Functional Correctness
• Code Quality Metrics
• Prompt Engineering
• Software Engineering

Code references

• features/copilot

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

Related on AIssential

• Beyond Problem Solving: UOJ-Bench for Evaluating Code Generation, Hacking, and Repair in Competitive Programming — LLMs can actively verify and debug competitive programming code, but current inference costs limit practical deployment.
• Rethinking Code Performance Benchmarks for LLMs — Existing LLM code performance benchmarks fail to expose true runtime differences, necessitating a multi-agent LLM framework for better test generation.
• From Rubrics to Recipe: Principle-Centric Benchmark for Evaluating Large Language Models — Evaluating LLMs with human-readable principles improves output quality and scales assessment beyond manual curation.
• Large Language Models for Code Generation from Multilingual Prompts: A Curated Benchmark and a Study on Code Quality — LLM code generation quality is biased by prompt language, with English not always superior.
• The TechBeat: Can LLMs Generate Quality Code? A 40,000-Line Experiment (1/6/2026) — LLMs generate sloppy code over time, requiring formal analysis and multi-model reviews for quality assurance.
• Why Open-Source & Chinese LLMs Lead Coding Benchmarks, But Struggle in the Real World? — Benchmark saturation inflates LLM coding scores, necessitating fresh evaluation methods like SWE-rebench for true generalization.

See Counsel's argued verdicts on the open AI decisions leaders are weighing →

Open in AIssential →

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