ICSE 2024
Fri 12 - Sun 21 April 2024 Lisbon, Portugal

Foundation Models (FM) like GPT-4 have given rise to FMware, FM-powered applications, which represent a new generation of software that is developed with new roles, assets, and paradigms. FMware has been widely adopted in both software engineering (SE) research (e.g., test generation) and industrial products (e.g., GitHub copilot), despite the numerous challenges introduced by the stochastic nature of FMs. Such challenges jeopardize the quality and trustworthiness of FMware. In our technical brief, we will present the latest research and industrial practices in engineering FMware, and discuss the SE challenges and opportunities facing both researchers and practitioners in the FMware era.

The brief is unique in that it is presented from an SE point of view, not an AI point-of-view ensuring that attendees are not bogged into complex mathematical and AI details unless they are essential for contextualizing the SE challenges and opportunities.

Fri 19 Apr

Displayed time zone: Lisbon change

11:00 - 12:30
Technical Briefings 6Technical Briefings at Vianna da Motta
Chair(s): Filipe Cogo Centre for Software Excellence, Huawei Canada, Ahmed E. Hassan Queen’s University, Dayi Lin Centre for Software Excellence, Huawei Canada, Gopi Krishnan Rajbahadur Centre for Software Excellence, Huawei, Canada
11:00
90m
Paper
Technical Brief on Software Engineering for FMware
Technical Briefings
Dayi Lin Centre for Software Excellence, Huawei Canada, Filipe Cogo Centre for Software Excellence, Huawei Canada, Gopi Krishnan Rajbahadur Centre for Software Excellence, Huawei, Canada, Ahmed E. Hassan Queen’s University
14:00 - 15:30
Technical Briefings 6 - (Part II)Technical Briefings at Vianna da Motta
14:00
90m
Paper
Technical Brief on Software Engineering for FMware
Technical Briefings
Dayi Lin Centre for Software Excellence, Huawei Canada, Filipe Cogo Centre for Software Excellence, Huawei Canada, Gopi Krishnan Rajbahadur Centre for Software Excellence, Huawei, Canada, Ahmed E. Hassan Queen’s University