Generative AI for Automated Software Code Synthesis and Validation (Journal of Generative Intelligence E: 3117-6429 P: 3117-6437, 2025)
Automated code synthesis -- the generation of correct, efficient, and secure software code from natural language specifications -- represents one of the highest-value practical applications of large generative AI models, with the potential to substantially accelerate software development productivity and reduce defect rates. While LLMs have demonstrated impressive code generation capability (HumanEval pass@1 scores exceeding 60% for state-of- the-art models), the deployment of AI-generated code in production software systems requires not only functional correctness but security soundness, computational efficiency, and maintainability -- quality dimensions that standard benchmarks inadequately capture. This paper proposes the Generative Code Synthesis and Validation (GCSV) framework, an end-to-end pipeline for AI-driven code generation, multi-dimensional validation, and iterative refinement that integrates four specialised components: a specification-aware code generator (SACG) that conditions LLM code generation on formal input-output specifications; a multi-dimensional code validator (MCV) that evaluates generated code on functional correctness, security, performance, and maintainability; a feedback-driven refinement controller (FDRC) that iteratively improves code based on validator feedback; and a code quality certifier (CQC) that issues formal quality certificates for code meeting all validation thresholds. GCSV is evaluated on three programming benchmark suites -- HumanEval, MBPP, and a novel Security-Performance-Maintainability benchmark (SPM-Bench) -- at four model scales (7B, 13B, 34B, 70B parameters). GCSV achieves HumanEval pass@1 = 71.8% (SD = 1.4%) at 7B scale -- a 48.4% improvement over naive LLM baseline -- and SPM-Bench composite quality score = 82.4% (SD = 2.8%) versus 54.6% for baseline. Security defect rate is reduced by 68.2% and computational efficiency improves by 24.6% through the FDRC refinement cycle. The study contributes the GCSV specification, the SPM-Bench benchmark, and empirical evidence that multi-dimensional validation with iterative refinement substantially outperforms single-pass code generation on production-relevant quality dimensions.