Large Language Models for Intelligent Code Generation in Software Engineering: A Systematic Review and Future Research Directions

Abstract

The proliferation of Large Language Models has catalyzed a paradigm shift in software engineering automation, yet existing literature reviews predominantly evaluate functional correctness while systematically neglecting security posture, maintainability, and multi-language deployment contexts. This study addresses the critical research gap regarding the absence of unified, multi-dimensional assessment protocols for LLM-generated code in production environments. Through a PRISMA-guided systematic review of 87 primary studies published between 2020 and 2025, this research examined architectural evolution, evaluation methodologies, and deployment barriers using a dual-phase qualitative and quantitative synthesis supported by a custom quality assessment framework. A five-criterion evaluation instrument was applied to ensure methodological rigor, with strong inter-rater reliability (Cohen’s kappa = 0.84). The findings reveal that decoder-only transformer architectures have achieved dominant performance on generative benchmarks, with Claude-3 Opus attaining 74.9% Pass@1 on HumanEval. However, only 12% of evaluated studies incorporated security metrics, fewer than 8% assessed maintainability, and benchmark contamination threatens the validity of reported generalization. The novelty of this work lies in proposing a unified evaluation framework that integrates functional correctness, Common Weakness Enumeration vulnerability scanning, and maintainability metrics, alongside a standardized multi-language protocol. The implications suggest that enterprise DevSecOps pipelines must embed static security analysis and cross-language quality gates before production integration. Future research should prioritize runtime verification, longitudinal productivity studies, and contamination-free benchmarks to ensure trustworthy deployment of generative AI in mission-critical software ecosystems.