AI-driven information systems development: LLM-based code generation approaches

Abstract

The paper presents a systematic review and empirical investigation of large language model (LLM)-based code generation systems covering the period 2022–2026, together with a proposed hybrid architecture that combines Retrieval-Augmented Generation (RAG) with a Mixture-of-Experts (MoE) routing strategy. The author surveys the three principal model families used for code generation — autoregressive decoders, masked language models, and encoder-decoder architectures — and synthesises benchmark results for leading systems such as Claude 3.7 Sonnet, GPT-4o, Gemini 2.5 Pro, DeepSeek V3 and Llama 4 Maverick across HumanEval, SWE-Bench, MBPP and LiveCodeBench. A Python-based prototype of the proposed hybrid model is implemented using QZhou-Embedding for query representation, a FAISS vector index for retrieval over a 10,000-snippet Python code corpus, three specialised MoE expert modules (Python, JavaScript, and security analysis), and an iterative self-reflection loop that verifies generated code through AST parsing and chain-of-thought semantic checks. Evaluated on the full 164-problem HumanEval benchmark, the hybrid model achieves a Pass@1 of 85%, exceeding CodeLlama by 15 percentage points and GPT-4o by 11 percentage points, while also producing code with a higher pylint score (8.5/10), lower cyclomatic complexity (2.3), and a higher Maintainability Index (85) than both baselines. The retrieval subsystem is shown to add less than 1% to end-to-end query latency while the generation stage accounts for roughly 94%, and the system scales acceptably under concurrent load. The author identifies three recurring limitation patterns — retrieval noise, self-reflection runtime overhead, and limited support for low-resource programming languages — and proposes fine-grained candidate re-ranking, adaptive reflection termination, and multilingual corpus extension as mitigation directions. The findings support the conclusion that combining retrieval grounding, task-specialised routing, and iterative verification yields measurable improvements in functional correctness and code quality over both commercial and open-source baselines.