arXiv:2604.11556v2 Announce Type: replace-cross Abstract: LLM-assisted software development has become increasingly prevalent, and can generate large-scale systems, such as compilers. It becomes crucial to strengthen the correctness of the generated code. However, automated reasoning for large-scale systems remains challenging due to code complexity. Hoare logic offers an approach to decomposing a large system into smaller components and reasoning about them separately (i.e., compositional reasoning). However, existing works still struggle to scale, because Hoare logic requires writing formal
The rapid development of LLM-driven code generation necessitates new approaches for ensuring correctness, making formal verification more critical and challenging.
Improving the correctness and reliability of LLM-generated large-scale systems is crucial for their adoption in critical applications, reducing technical debt and security risks.
The ability to formally verify complex, LLM-generated code could significantly enhance developer productivity and system reliability, rather than just generating quantity over quality.
- · Software developers
- · AI-driven software engineering platforms
- · High-assurance software sectors
- · Formal methods researchers
- · Companies reliant on informal testing methods for complex AI-generated code
- · Developers struggling with debugging large, unverified codebases
More reliable and trustworthy AI-generated software across various domains.
Increased adoption of LLMs for generating critical infrastructure code, accelerating the AI agent paradigm.
Enhanced security and reduced incidence of software bugs and vulnerabilities due to verifiable AI-generated code.
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Read at arXiv cs.AI