Detecting UX smells in Visual Studio Code using LLMs
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866915816642969600 |
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| author | Rodriguez, Andrés Gardey, Juan Cruz Garrido, Alejandra |
| author_facet | Rodriguez, Andrés Gardey, Juan Cruz Garrido, Alejandra |
| contents | Integrated Development Environments shape developers' daily experience, yet the empirical study of their usability and user experience (UX) remains limited. This work presents an LLM-assisted approach to detecting UX smells in Visual Studio Code by mining and classifying user-reported issues from the GitHub repository. Using a validated taxonomy and expert review, we identified recurring UX problems that affect the developer experience. Our results show that the majority of UX smells are concentrated in informativeness, clarity, intuitiveness, and efficiency, qualities that developers value most. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_22020 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Detecting UX smells in Visual Studio Code using LLMs Rodriguez, Andrés Gardey, Juan Cruz Garrido, Alejandra Software Engineering Human-Computer Interaction Integrated Development Environments shape developers' daily experience, yet the empirical study of their usability and user experience (UX) remains limited. This work presents an LLM-assisted approach to detecting UX smells in Visual Studio Code by mining and classifying user-reported issues from the GitHub repository. Using a validated taxonomy and expert review, we identified recurring UX problems that affect the developer experience. Our results show that the majority of UX smells are concentrated in informativeness, clarity, intuitiveness, and efficiency, qualities that developers value most. |
| title | Detecting UX smells in Visual Studio Code using LLMs |
| topic | Software Engineering Human-Computer Interaction |
| url | https://arxiv.org/abs/2602.22020 |