mlsanityData to 7 Oct 2026

LLM-Powered Workflow Optimization for Multidisciplinary Software Development: An Automotive Industry Case Study

arXiv 2603.21439 ↗First posted 22 Mar 2026cs.SE cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Multidisciplinary Software Development (MSD) requires domain experts and developers to collaborate across incompatible formalisms and separate artifact sets. Today, even with AI coding assistants like GitHub Copilot, this process remains inefficient; individual coding tasks are semi-automated, but the workflow connecting domain knowledge to implementation is not. Developers and experts still lack a shared view, resulting in repeated coordination, clarification rounds, and error-prone handoffs. We address this gap through a graph-based workflow optimization approach that progressively replaces manual coordination with LLM-powered services, enabling incremental adoption without disrupting established practices. We evaluate our approach on textttspapi, a production in-vehicle API system at Volvo Group involving 192 endpoints, 420 properties, and 776 CAN signals across six functional domains. The automated workflow achieves 93.7% F1 score while reducing per-API development time from approximately 5 hours to under 7 minutes, saving an estimated 979 engineering hours. In production, the system received high satisfaction from both domain experts and developers, with all participants reporting full satisfaction with communication efficiency.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 52 references that are in the map. First posted on arXiv 22 Mar 2026.

Builds on

20 of 52 references.

References · 20

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelinescited by 803
  3. 2022Can Foundation Models Wrangle Your Data?cited by 173
  4. 2024SynCode: LLM Generation with Grammar Augmentationcited by 66
  5. 2024Grammar-Aligned Decodingcited by 54
  6. 2021The future of human-AI collaboration: a taxonomy of design knowledge for hybrid intelligence systemscited by 39
  7. 2021How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Studycited by 29
  8. 2023Leveraging Large Language Models to Improve REST API Testingcited by 20
  9. 2024Prototyping with Prompts: Emerging Approaches and Challenges in Generative AI Design for Collaborative Software Teamscited by 16
  10. 2025A Framework for Testing and Adapting REST APIs as LLM Toolscited by 9
  11. 2025Human and Machine: How Software Engineers Perceive and Engage with AI-Assisted Code Reviews Compared to Their Peerscited by 8
  12. 2025Automating a Complete Software Test Process Using LLMs: An Automotive Case Studycited by 8
8 more
  1. 2025iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answeringcited by 8
  2. 2025Plugging Schema Graph into Multi-Table QA: A Human-Guided Framework for Reducing LLM Reliancecited by 7
  3. 2025LLM-Generated Microservice Implementations from RESTful API Definitionscited by 5
  4. 2023Automotive Perception Software Development: An Empirical Investigation into Data, Annotation, and Ecosystem Challengescited by 4
  5. 2024Towards Effective Collaboration between Software Engineers and Data Scientists developing Machine Learning-Enabled Systemscited by 3
  6. 2025LRASGen: LLM-based RESTful API Specification Generationcited by 3
  7. 2025When Domains Collide: An Activity Theory Exploration of Cross-Disciplinary Collaborationcited by 1
  8. 2025Domain-Adapted Pre-trained Language Models for Implicit Information Extraction in Crash Narrativescited by 1

Built on by

Not cited yet.