LLM-Powered Workflow Optimization for Multidisciplinary Software Development: An Automotive Industry Case Study
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 e...
| Authors | Shuai Wang, Yinan Yu, Earl Barr et al. |
|---|---|
| Primary category | cs.SE |
| Published | 22 March 2026 |
| Last updated at source | 30 March 2026 |
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