Towards specification-driven LLM-based generation of embedded automotive software
2025 (English)In: Bridging the gap between AI and reality: second international conference, AISoLA 2024, Crete, Greece, October 30 – November 3, 2024, proceedings / [ed] Bernhard Steffen, Springer, 2025, p. 125-144Conference paper, Published paper (Refereed)
Abstract [en]
The paper studies how code generation by LLMs can be combined with formal verification to produce critical embedded software. The first contribution is a general framework, spec2code, in which LLMs are combined with different types of critics that produce feedback for iterative backprompting and fine-tuning. The second contribution presents a first feasibility study, where a minimalistic instantiation of spec2code, without iterative backprompting and fine-tuning, is empirically evaluated using three industrial case studies from the heavy vehicle manufacturer Scania. The goal is to automatically generate industrial-quality code from specifications only. Different combinations of formal ACSL specifications and natural language specifications are explored. The results indicate that formally correct code can be generated even without the application of iterative backprompting and fine-tuning.
Place, publisher, year, edition, pages
Springer, 2025. p. 125-144
Series
Lecture Notes in Computer Science (LNCS), ISSN 0302-9743, E-ISSN 1611-3349 ; 15217
Keywords [en]
Automated Software Engineering, Code Generation, Formal Verification, Large Language Models
National Category
Software Engineering Artificial Intelligence
Identifiers
URN: urn:nbn:se:umu:diva-234883DOI: 10.1007/978-3-031-75434-0_9Scopus ID: 2-s2.0-85215782156ISBN: 9783031754333 (print)ISBN: 9783031754340 (electronic)OAI: oai:DiVA.org:umu-234883DiVA, id: diva2:1935766
Conference
2nd International Conference on Bridging the Gap Between AI and Reality, AISoLA 2024, Crete, Greece, October 30 - November 3, 2024
2025-02-072025-02-072025-02-07Bibliographically approved