Rethinking Software Engineering in the Foundation Model Era: A Curated Catalogue of Challenges in the Development of Trustworthy FMware
Ahmed E. Hassan, Dayi Lin, Gopi Krishnan Rajbahadur, Keheliya Gallaba,, Filipe R. Cogo, Boyuan Chen, Haoxiang Zhang, Kishanthan Thangarajah, Gustavo, Ansaldi Oliva, Jiahuei Lin, Wali Mohammad Abdullah, Zhen Ming Jiang

TL;DR
This paper identifies key software engineering challenges in developing trustworthy foundation model-based software (FMware), introduces FMArts platform to address these issues, and shares practical lessons from real-world application.
Contribution
It presents a curated catalogue of 10 challenges in FMware development, along with the FMArts platform for engineering trustworthy FMware and insights from its industrial deployment.
Findings
Identified 10 key challenges in FMware development.
Demonstrated FMArts platform enabling timely complex FMware creation.
Shared lessons learned from industrial application of FMArts.
Abstract
Foundation models (FMs), such as Large Language Models (LLMs), have revolutionized software development by enabling new use cases and business models. We refer to software built using FMs as FMware. The unique properties of FMware (e.g., prompts, agents, and the need for orchestration), coupled with the intrinsic limitations of FMs (e.g., hallucination) lead to a completely new set of software engineering challenges. Based on our industrial experience, we identified 10 key SE4FMware challenges that have caused enterprise FMware development to be unproductive, costly, and risky. In this paper, we discuss these challenges in detail and state the path for innovation that we envision. Next, we present FMArts, which is our long-term effort towards creating a cradle-to-grave platform for the engineering of trustworthy FMware. Finally, we (i) show how the unique properties of FMArts enabled us…
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Taxonomy
TopicsSecurity and Verification in Computing · Business Process Modeling and Analysis · Advanced Software Engineering Methodologies
MethodsSparse Evolutionary Training
