Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2407.07269 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866911950685863936 |
|---|---|
| author | Marchezan, Luciano Assunção, Wesley K. G. Herac, Edvin Egyed, Alexander |
| author_facet | Marchezan, Luciano Assunção, Wesley K. G. Herac, Edvin Egyed, Alexander |
| contents | Model-Based Engineering (MBE) has streamlined software development by focusing on abstraction and automation. The adoption of MBE in Maintenance and Evolution (MBM&E), however, is still limited due to poor tool support and a lack of perceived benefits. We argue that Generative Artificial Intelligence (GenAI) can be used as a means to address the limitations of MBM&E. In this sense, we argue that GenAI, driven by Foundation Models, offers promising potential for enhancing MBM&E tasks. With this possibility in mind, we introduce a research vision that contains a classification scheme for GenAI approaches in MBM&E considering two main aspects: (i) the level of augmentation provided by GenAI and (ii) the experience of the engineers involved. We propose that GenAI can be used in MBM&E for: reducing engineers' learning curve, maximizing efficiency with recommendations, or serving as a reasoning tool to understand domain problems. Furthermore, we outline challenges in this field as a research agenda to drive scientific and practical future solutions. With this proposed vision, we aim to bridge the gap between GenAI and MBM&E, presenting a structured and sophisticated way for advancing MBM&E practices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_07269 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Model-based Maintenance and Evolution with GenAI: A Look into the Future Marchezan, Luciano Assunção, Wesley K. G. Herac, Edvin Egyed, Alexander Software Engineering Model-Based Engineering (MBE) has streamlined software development by focusing on abstraction and automation. The adoption of MBE in Maintenance and Evolution (MBM&E), however, is still limited due to poor tool support and a lack of perceived benefits. We argue that Generative Artificial Intelligence (GenAI) can be used as a means to address the limitations of MBM&E. In this sense, we argue that GenAI, driven by Foundation Models, offers promising potential for enhancing MBM&E tasks. With this possibility in mind, we introduce a research vision that contains a classification scheme for GenAI approaches in MBM&E considering two main aspects: (i) the level of augmentation provided by GenAI and (ii) the experience of the engineers involved. We propose that GenAI can be used in MBM&E for: reducing engineers' learning curve, maximizing efficiency with recommendations, or serving as a reasoning tool to understand domain problems. Furthermore, we outline challenges in this field as a research agenda to drive scientific and practical future solutions. With this proposed vision, we aim to bridge the gap between GenAI and MBM&E, presenting a structured and sophisticated way for advancing MBM&E practices. |
| title | Model-based Maintenance and Evolution with GenAI: A Look into the Future |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2407.07269 |