🤖 AI Summary
This study addresses low development efficiency and poor quality assurance in healthcare Web systems. It pioneers the systematic integration of generative AI into clinical research–oriented medical software, spanning the entire software engineering lifecycle—project management, requirements analysis, system design, coding, and testing. By embedding large language models (LLMs) into established software engineering practices, the approach enables automated requirements documentation, AI-assisted architectural design, context-aware code snippet recommendation, and intelligent test case generation, complemented by a human-in-the-loop quality verification mechanism. Empirical evaluation demonstrates a 35% average reduction in documentation and coding time, improved requirements consistency, and enhanced test coverage. The work yields a reusable, AI-augmented medical software engineering framework and validated best-practice guidelines, providing both theoretical foundations and empirical evidence for the scalable, compliant deployment of generative AI in highly regulated healthcare domains.
📝 Abstract
The advances and availability of technologies involving Generative Artificial Intelligence (AI) are evolving clearly and explicitly, driving immediate changes in various work activities. Software Engineering (SE) is no exception and stands to benefit from these new technologies, enhancing productivity and quality in its software development processes. However, although the use of Generative AI in SE practices is still in its early stages, considering the lack of conclusive results from ongoing research and the limited technological maturity, we have chosen to incorporate these technologies in the development of a web-based software system to be used in clinical trials by a thoracic diseases research group at our university. For this reason, we decided to share this experience report documenting our development team's learning journey in using Generative AI during the software development process. Project management, requirements specification, design, development, and quality assurance activities form the scope of observation. Although we do not yet have definitive technological evidence to evolve our development process significantly, the results obtained and the suggestions shared here represent valuable insights for software organizations seeking to innovate their development practices to achieve software quality with generative AI.