Exploring and Evaluating AI algorithm for Automated Code Generation
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2026
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| author | Hiral Girishkumar Pandya Dr. Nayan Soni |
| author_facet | Hiral Girishkumar Pandya Dr. Nayan Soni |
| contents | The development of sophisticated Artificial Intelligence (AI) techniques, especially deep learning-based architectures, has led to a considerable evolution in automated code generation. The ability to produce syntactically accurate and semantically relevant source code from natural language prompts has been established by transformer-based Large Language Models (LLMs) trained on large code corpora[1], [2]. This study investigates the main AI algorithms used in automated code production, such as hybrid neuro-symbolic techniques, Transformer structures, and sequence-to-sequence models[5, 7]. Incorporating syntactic validity, semantic correctness, functional accuracy, computational efficiency, and security robustness, a methodical evaluation approach is suggested. Using common benchmarks, comparative studies are conducted among representative models including Codex[2], CodeT5[3], and AlphaCode[4]. Although Transformer-based models are highly accurate in function-level generation tasks, the results show that they still have shortcomings in long-context reasoning, security assurance, and domain-specific reliability. In addition to offering a systematic evaluation approach, this paper suggests future lines of inquiry for reliable, safe, and explicable AI-driven code synthesis. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19627635 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Exploring and Evaluating AI algorithm for Automated Code Generation Hiral Girishkumar Pandya Dr. Nayan Soni Artificial Intelligence Automated Code Generation Program Synthesis Transformer Models Large Language Models Neural Program Synthesis Code Evaluation Metrics Software Engineering Automation The development of sophisticated Artificial Intelligence (AI) techniques, especially deep learning-based architectures, has led to a considerable evolution in automated code generation. The ability to produce syntactically accurate and semantically relevant source code from natural language prompts has been established by transformer-based Large Language Models (LLMs) trained on large code corpora[1], [2]. This study investigates the main AI algorithms used in automated code production, such as hybrid neuro-symbolic techniques, Transformer structures, and sequence-to-sequence models[5, 7]. Incorporating syntactic validity, semantic correctness, functional accuracy, computational efficiency, and security robustness, a methodical evaluation approach is suggested. Using common benchmarks, comparative studies are conducted among representative models including Codex[2], CodeT5[3], and AlphaCode[4]. Although Transformer-based models are highly accurate in function-level generation tasks, the results show that they still have shortcomings in long-context reasoning, security assurance, and domain-specific reliability. In addition to offering a systematic evaluation approach, this paper suggests future lines of inquiry for reliable, safe, and explicable AI-driven code synthesis. |
| title | Exploring and Evaluating AI algorithm for Automated Code Generation |
| topic | Artificial Intelligence Automated Code Generation Program Synthesis Transformer Models Large Language Models Neural Program Synthesis Code Evaluation Metrics Software Engineering Automation |
| url | https://doi.org/10.5281/zenodo.19627635 |