Forecasting Developer Environments with GenAI: A Research Perspective

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Authors: Raula Gaikovina Kula, Christoph Treude, Xing Hu, Sebastian Baltes, Earl T. Barr, Kelly Blincoe, Fabio Calefato, Junjie Chen, Marc Cheong, Youmei Fan, Daniel M. Germán, Marco Aurélio Gerosa
+19 more Jin L. C. Guo, Shinpei Hayashi, Robert Hirschfeld, Reid Holmes, Yintong Huo, Takashi Kobayashi, Michele Lanza, Zhongxin Liu, Olivier Nourry, Nicole Novielli, Denys Poshyvanyk, Shinobu Saito, Kazumasa Shimari, Igor Steinmacher, Mairieli Santos Wessel, Markus Wagner, Annie Vella, Laurie A. Williams, Xin Xia
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Abstract

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.

Fields of Study & Subjects

Software Engineering Research Software Engineering Techniques and Practices Advanced Software Engineering Methodologies

Publication details

Journal / source
arXiv (Cornell University)
Publication date
7 February 2026
Language
EN
Document type
Preprint