CoLSM: Collaborative Large and Small Models for Automatic Software Generation

Abstract

Large language models (LLMs) support human-in-the-loop code development by rapidly generating high-quality code snippets. However, they still face prominent challenges in fast and efficient deployment on edge environments. Such challenges mainly involve heavy computation costs, poor domain accuracy, unbalanced collaboration efficiency and inconsistent cross-model knowledge. This study proposes CoLSM, a new collaboration mechanism guided by mixture experts for automatic software generation. It establishes a hierarchical and iterative working pipeline. A mixture-expert router assigns tasks dynamically. Large models take charge of system architecture and complex logic design. Domain-adapted small models refine code details, optimize resource usage and ensure security compliance. This mechanism integrates an abstract syntax tree based synchronization module to resolve cross-model conflicts and embeds a quality feedback loop to support adaptive iterative optimization. We evaluate the proposed CoLSM on a self-built multi-scenario software generation dataset. Experimental results demonstrate that CoLSM improves software generation accuracy and functional consistency by 4.3% and 5.7%, respectively. It also reduces inference latency by 20.3% and energy consumption by 14.8%. CoLSM effectively combines the respective advantages of large and small models. It realizes accurate and low-cost automatic software generation and provides reliable technical support for agile automated software development.