Strengthening Programming Comprehension in Large Language Models through Code Generation

• URL: http://arxiv.org/abs/2508.12620v1
• Date: Mon, 18 Aug 2025 04:33:03 GMT
• Title: Strengthening Programming Comprehension in Large Language Models through Code Generation
• Authors: Xiaoning Ren, Qiang Hu, Wei Ma, Yan Li, Yao Zhang, Lingxiao Jiang, Yinxing Xue,
• Abstract summary: Large language models (LLMs) have recently shown impressive results on diverse code-related tasks.<n>Their grasp of fundamental programming concepts, such as data flow and control flow, remains shallow, leading to fragile performance when code requires deeper reasoning.<n>This work introduces a counterfactual code augmentation framework combined with concept-aware tuning, designed to guide LLMs toward stronger conceptual understanding.
• Score: 23.72685095718304
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have recently shown impressive results on diverse code-related tasks, benefiting from large-scale training and instruction tuning. However, studies reveal that their grasp of fundamental programming concepts, such as data flow and control flow, remains shallow, leading to fragile performance when code requires deeper reasoning. This limitation restricts the practical adoption of LLMs in real-world software development. To address this issue, this work introduces a counterfactual code augmentation framework combined with concept-aware tuning, designed to guide LLMs toward stronger conceptual understanding. Comprehensive evaluation across multiple models and benchmarks demonstrates the effectiveness of the proposed approach.

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