Automatic Code Generation Techniques: A Systematic Literature Review

As modern software systems become complex and the demand for rapid development cycles increases, automatic code generation techniques have attained a prominent focus in academic research and industrial practice. These techniques can significantly reduce human error, increase productivity, and ensure consistency across large codebases. However, the task of generating code automatically presents significant challenges. In this study, we investigate, identify, and analyze the existing automatic techniques for generating code from various input formats, highlighting their efficiencies and areas for potential improvement. A Systematic Literature Review (SLR) is conducted to systematically summarize and review 76 primary studies related to automatic code generation in the software engineering domain. The selected studies are investigated from several dimensions: paradigms, techniques, input types, intermediate representations, tool support, targeted programming languages, and validation methods, including performance metrics, datasets, and benchmarking status. Our investigation identified 12 main techniques, categorized into five paradigms, where the Model-to-Code paradigm and model-driven techniques are the most prevalent. Notably, 57% of the studies utilized Java, and a limited number of studies showed multilingual support. Furthermore, 72% of the selected studies did not compare their results with existing techniques, and 17% lacked validation of the proposed techniques. We also noticed a lack of detailed information about the datasets used in the validation process, where 52% of the studies omitted these details. This SLR provides several recommendations to enhance methodological rigor in future research, and it highlights opportunities for leveraging emerging technologies to improve the efficiency of the identified automatic code generation techniques.