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TIPS: A Prompt Engineering Framework for Code Classification and Generation on Resource-Constrained Systems
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DOI：10.1109/ACCESS.2026.3658502.png)
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Abstract
En 中文
Recent advances in AI-assisted programming have demonstrated the potential of Large Language Models (LLMs) in complex code classification and generation. However, their high computational demands and reliance on large-scale datasets limit deployment in resource-constrained environments such as educational tools, edge devices, and small-scale development settings. To address these challenges, this paper introduces the Template-Integrated Prompting System (TIPS), a modular prompt engineering framework designed for lightweight and efficient code analysis. TIPS integrates small-model training, semantically guided demonstration selection, and structured prompt templates to construct an interpretable reasoning pipeline that combines few-shot learning with Chain-of-Thought (CoT) reasoning. Experiments on a benchmark dataset of multithreaded Pthread programs show that TIPS achieves higher precision, recall, and F1-scores than traditional classifiers and prompt-based baselines, even under class imbalance. These results demonstrate that model effectiveness depends not only on scale but also on semantic guidance and structured reasoning. TIPS provides a scalable and interpretable solution, with potential applications in education, software development, and intelligent assistance systems, particularly in resource-constrained environments.
Keywords:
Chain-of-thought
code classification
constrained environments
few-shot learning
large language models (LLMs)
prompt engineering
resource-constrained environments