Forecasting Developer Environments with GenAI: A Research Perspective
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arXiv
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| Format: | Preprint |
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2026
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| author | Kula, Raula Gaikovina Treude, Christoph Hu, Xing Baltes, Sebastian Barr, Earl T. Blincoe, Kelly Calefato, Fabio Chen, Junjie Cheong, Marc Fan, Youmei German, Daniel M. Gerosa, Marco Guo, Jin L. C. Hayashi, Shinpei Hirschfeld, Robert Holmes, Reid Huo, Yintong Kobayashi, Takashi Lanza, Michele Liu, Zhongxin Nourry, Olivier Novielli, Nicole Poshyvanyk, Denys Saito, Shinobu Shimari, Kazumasa Steinmacher, Igor Wessel, Mairieli Wagner, Markus Vella, Annie Williams, Laurie Xia, Xin |
| author_facet | Kula, Raula Gaikovina Treude, Christoph Hu, Xing Baltes, Sebastian Barr, Earl T. Blincoe, Kelly Calefato, Fabio Chen, Junjie Cheong, Marc Fan, Youmei German, Daniel M. Gerosa, Marco Guo, Jin L. C. Hayashi, Shinpei Hirschfeld, Robert Holmes, Reid Huo, Yintong Kobayashi, Takashi Lanza, Michele Liu, Zhongxin Nourry, Olivier Novielli, Nicole Poshyvanyk, Denys Saito, Shinobu Shimari, Kazumasa Steinmacher, Igor Wessel, Mairieli Wagner, Markus Vella, Annie Williams, Laurie Xia, Xin |
| contents | Generative Artificial Intelligence (GenAI) models are achieving remarkable performance in various tasks, including code generation, testing, code review, and program repair. The ability to increase the level of abstraction away from writing code has the potential to change the Human-AI interaction within the integrated development environment (IDE). To explore the impact of GenAI on IDEs, 33 experts from the Software Engineering, Artificial Intelligence, and Human-Computer Interaction domains gathered to discuss challenges and opportunities at Shonan Meeting 222, a four-day intensive research meeting. Four themes emerged as areas of interest for researchers and practitioners. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_07412 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Forecasting Developer Environments with GenAI: A Research Perspective Kula, Raula Gaikovina Treude, Christoph Hu, Xing Baltes, Sebastian Barr, Earl T. Blincoe, Kelly Calefato, Fabio Chen, Junjie Cheong, Marc Fan, Youmei German, Daniel M. Gerosa, Marco Guo, Jin L. C. Hayashi, Shinpei Hirschfeld, Robert Holmes, Reid Huo, Yintong Kobayashi, Takashi Lanza, Michele Liu, Zhongxin Nourry, Olivier Novielli, Nicole Poshyvanyk, Denys Saito, Shinobu Shimari, Kazumasa Steinmacher, Igor Wessel, Mairieli Wagner, Markus Vella, Annie Williams, Laurie Xia, Xin Software Engineering Generative Artificial Intelligence (GenAI) models are achieving remarkable performance in various tasks, including code generation, testing, code review, and program repair. The ability to increase the level of abstraction away from writing code has the potential to change the Human-AI interaction within the integrated development environment (IDE). To explore the impact of GenAI on IDEs, 33 experts from the Software Engineering, Artificial Intelligence, and Human-Computer Interaction domains gathered to discuss challenges and opportunities at Shonan Meeting 222, a four-day intensive research meeting. Four themes emerged as areas of interest for researchers and practitioners. |
| title | Forecasting Developer Environments with GenAI: A Research Perspective |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2602.07412 |