International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Modern software development faces a persistent challenge: translating conceptual requirements into functional backend systems demands specialized technical knowledge that excludes many potential innovators. This paper presents SmartAPIForge, a new platform conceived to overcome this accessibility barrier by intelligent automation of REST API creation. Unlike currently available tools that focus on user interface generation, our work puts greater emphasis on developing the back-end infrastructure with input in natural language. The platform architecture includes a variety of technological advancements in order to realize dependable automation. First, we apply modern language models to interpret what the user needs and then generate appropriate code structures. Second, micro-virtual machines based on Firecracker provide isolated execution environments and protect against security flaws during testing and code development. Third, we keep record of development history during project life cycles and enable team collaboration by integrating with GitHub's version control system.To empirically validate our system, we tested it against 3,079 different API creation tasks derived from real-world development scenarios. The findings show that deployment processes were successful 96% of the time, and generated code compiled successfully in 94% of cases.Furthermore, validation checks against industry standards were passed by 97% of generated OpenAPI specifications. The platform generated everything from the initial prompt to a deployed, documented API within 60 seconds for 95% of test cases. These results show that automated methods can greatly lower the barriers to expertise and time spent on API development. Through careful testing and validation, this work proves that the technology is viable while keeping code quality intact. SmartAPIForge represents a useful step toward making backend development accessible to more users by lowering entry barriers without losing engineering standards.

Keywords: Automated Testing, Code Generation, GitHub Integration, Large Language Model Technologies, Natural Language Processing Techniques

Downloads: | DOI: 10.17148/IJARCCE.2025.141262