Lessons Learned from the Use of Generative AI in Engineering and Quality Assurance of a WEB System for Healthcare
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912683217911808 |
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| author | Travassos, Guilherme H. Rocha, Sabrina Feitosa, Rodrigo Assis, Felipe Goncalves, Patricia Gheventer, Andre Galeno, Larissa Sasse, Arthur Guimaraes, Julio Cesar Brito, Carlos Wieland, Joao Pedro |
| author_facet | Travassos, Guilherme H. Rocha, Sabrina Feitosa, Rodrigo Assis, Felipe Goncalves, Patricia Gheventer, Andre Galeno, Larissa Sasse, Arthur Guimaraes, Julio Cesar Brito, Carlos Wieland, Joao Pedro |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_00658 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Lessons Learned from the Use of Generative AI in Engineering and Quality Assurance of a WEB System for Healthcare Travassos, Guilherme H. Rocha, Sabrina Feitosa, Rodrigo Assis, Felipe Goncalves, Patricia Gheventer, Andre Galeno, Larissa Sasse, Arthur Guimaraes, Julio Cesar Brito, Carlos Wieland, Joao Pedro Software Engineering Artificial Intelligence Emerging Technologies 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. |
| title | Lessons Learned from the Use of Generative AI in Engineering and Quality Assurance of a WEB System for Healthcare |
| topic | Software Engineering Artificial Intelligence Emerging Technologies |
| url | https://arxiv.org/abs/2511.00658 |