PROBE: Benchmarking Code Generation in Large Language Models

• URL: http://arxiv.org/abs/2607.13820v1
• Date: Wed, 15 Jul 2026 13:28:18 GMT
• Title: PROBE: Benchmarking Code Generation in Large Language Models
• Abstract summary: Large Language Models (LLMs) are increasingly being used in everyday software engineering tasks.<n>LLMs are far from perfect, making evaluation essential to understand their strengths and limitations.<n>We introduce PROBE, a benchmark framework that establishes a systematic structure built on diverse and well-defined metrics.
• Score: 0.8921166277011344
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) are increasingly being used in everyday software engineering tasks, particularly in automated code generation. Despite their widespread adoption, these models remain far from perfect, making systematic and fair evaluation essential to understand their strengths and limitations. In the context of code generation, existing benchmarks are limited: they often target a single programming language and rely primarily on unit test outcomes, while overlooking other critical dimensions such as the overall quality of the generated code and its closeness to a valid solution. To address these gaps, we introduce PROBE, an extensible benchmark framework that, unlike prior work, establishes a systematic structure built on diverse and well-defined metrics, representative workloads, varied prompt templates, and a robust experimental procedure. In practice, the code generated by the LLMs is evaluated along three complementary dimensions: functional correctness, proximity to valid solutions, and code quality, enabling a comprehensive assessment of performance. We use PROBE to evaluate four open-source and two proprietary models under three prompting strategies across five programming languages. We further complement this analysis with a study of common errors in the code and provide concrete examples, offering clearer insight into where LLMs tend to struggle. Our findings show that, while LLMs achieve promising results, they struggle with harder problems and, in the case of smaller models, with programming languages that have fewer available resources for training, and they often fail due to fundamental and easily avoidable errors that underscore the unreliability of automatically generated code.

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