[2603.21439] LLM-Powered Workflow Optimization for Multidisciplinary Software Development: An Automotive Industry Case Study

Computer Science > Software Engineering arXiv:2603.21439 (cs) [Submitted on 22 Mar 2026] Title:LLM-Powered Workflow Optimization for Multidisciplinary Software Development: An Automotive Industry Case Study Authors:Shuai Wang, Yinan Yu, Earl Barr, Dhasarathy Parthasarathy View a PDF of the paper titled LLM-Powered Workflow Optimization for Multidisciplinary Software Development: An Automotive Industry Case Study, by Shuai Wang and 3 other authors View PDF HTML (experimental) Abstract: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 \texttt{spapi}, 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 approximat...