Forecasting Developer Environments with GenAI: A Research Perspective
The University of Osaka（大阪大学）；Singapore Management University（新加坡管理大学）；Zhejiang University（浙江大学）；Heidelberg University（海德堡大学）；University College London（伦敦大学学院）；University of Auckland（奥克兰大学）；University of Bari（巴里大学）；Tianjin University（天津大学）；University of Melbourne（墨尔本大学）；Nara Institute of Science and Technology（奈良先端科学技术大学院大学）；University of Victoria（维多利亚大学）；Northern Arizona University（北亚利桑那大学）
Raula Gaikovina Kula, Christoph Treude, Xing Hu, Sebastian Baltes, Earl T. Barr, Kelly Blincoe, Fabio Calefato, Junjie Chen, Marc Cheong, Youmei Fan, Daniel M. … 展开作者
Raula Gaikovina Kula, Christoph Treude, Xing Hu, Sebastian Baltes, Earl T. Barr, Kelly Blincoe, Fabio Calefato, Junjie Chen, Marc Cheong, Youmei Fan, Daniel M. German, Marco Gerosa, 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 Wessel, Markus Wagner, Annie Vella, Laurie Williams, Xin Xia
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.
Kyushu University（九州大学）；Nara Institute of Science and Technology（奈良先端科学技术大学院大学）；University of Bari（巴里大学）；Singapore Management University（新加坡管理大学）；University of Osaka（大阪大学）；Shinshu University（信州大学）；Heidelberg University（海德堡大学）
"Always Nice and Confident, Sometimes Wrong": Developer's Experiences Engaging Large Language Models (LLMs) Versus Human-Powered Q&A Platforms for Coding Support
Leveraging Large Language Models for Code Translation and Software Development in Scientific Computing
Institute for Clarity in Documentation（清晰文档研究所）；Inria Paris-Rocquencourt（巴黎-罗克琴特研究所）；Rajiv Gandhi University（拉贾·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕勒尔研究实验室）；Argonne National Laboratory（阿贡国家实验室）
Breaking New Ground in Software Defect Prediction: Introducing Practical and Actionable Metrics with Superior Predictive Power for Enhanced Decision-Making
