Artificial Intelligence (AI) is transforming software engineering by enabling automated code generation, intelligent debugging, and quality assurance workflows. This paper presents CodeSynth+, a hybrid framework that integrates transformer based code generation with Graph Neural Network (GNN)-based semantic analysis to produce syntactically correct, semantically validated, and secure multi-language code (Python, Java, C++). CodeSynth+ operates in an iterative feedback loop: natural language requirements are converted to initial code by a fine- tuned transformer, the code is parsed into Abstract Syntax Trees (ASTs) and semantic graphs, and a GNN inspects structural and data-flow properties to detect logic errors and vulnerabilities. We describe dataset construction (public code corpora, competitive programming solutions, and curated GitHub projects), formalize evaluation metrics (Syntactic Accuracy, Semantic Precision, Maintainability Index, Security Vulnerability Score, Regeneration Success Rate, and Time to Production), and detail baseline configurations (CodeT5, Codex-like evaluation, and SonarQube rules). Ten experiments demonstrate consistent improvements versus transformer only baselines: increased semantic accuracy and vulnerability detection, improved maintainability scores, and reduced time-to-production. We provide statistical validation (multiple seeds, means ± std, confidence intervals, and significance testing) and reproducibility artifacts (scripts and configs) in the supplemental repository. CodeSynth+ represents a step toward autonomous, interpretable, and secure code generation in modern software engineering.
Rupendra et al. (Wed,) studied this question.