mlsanityData to 7 Oct 2026

Rethinking Software Engineering in the Foundation Model Era: A Curated Catalogue of Challenges in the Development of Trustworthy FMware

arXiv 2402.15943 ↗First posted 25 Feb 2024cs.SE cs.AIcited by 6

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Abstract (arXiv)

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 to design and develop a complex FMware for a large customer in a timely manner and (ii) discuss the lessons that we learned in doing so. We hope that the disclosure of the aforementioned challenges and our associated efforts to tackle them will not only raise awareness but also promote deeper and further discussions, knowledge sharing, and innovative solutions across the software engineering discipline.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 49 of its 109 references that are in the map. First posted on arXiv 25 Feb 2024.

Builds on

49 of 109 references.

References · 49

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37 more
  1. 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,020
  2. 2023DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelinescited by 803
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  4. 2023Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolutioncited by 521
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 2; 2026: 1.

6 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2024From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap
  2. 2026Understanding How Enterprises Adopt the Model Context Protocol for LLM-Driven Software Engineering
  3. 2025Towards Conversational Development Environments: Using Theory-of-Mind and Multi-Agent Architectures for Requirements Refinement
  4. 2026An Empirical Study of Model Context Protocol Applications
  5. 2026From What to How: Bridging User Requirements with Software Development Using Large Language Models
  6. 2026The Evolution of Tool Use in LLM Agents: From Single-Tool Call to Multi-Tool Orchestration
  7. 2026Model Context Protocol (MCP) Tool Descriptions Are Smelly! Towards Improving AI Agent Efficiency with Augmented MCP Tool Descriptions
  8. 2025Challenges in Testing Large Language Model Based Software: A Faceted Taxonomy