BRIDGE is presented, a structured prompting framework that decomposes verification into three interconnected domains: Code (implementations), Specifications (formal intent), and Theorem State-ments (constructive correctness claims), and elicits domain-specific intermediate reasoning to connect them.
The results indicate that functional correctness in code generation can be meaningfully improved without modifying the backbone architecture, by jointly optimizing how tasks are prompted, how the model is adapted, and how final outputs are selected.
VeriRefine progressively refines the prose specification into an explicit, schema-constrained account of design intent, expressed as per-signal Abstract Signal Transition Functions (ASTFs) that commit each signal's logic style, clock domain, and reset behavior before any code exists and ground every behavior in a verba...
This work addresses three interconnected challenges in building behaviorally-grounded code generation: retrieving not just what symbols are, but how they behave through usage patterns; adapting practices across contexts by separating behavioral intent from implementation details; and enforcing semantic properties durin...
A test-driven pipeline is presented that extracts functional requirements (FR) from a problem description, resolves dependencies, maps them into a modular Model-View-Controller (MVC) structure, and generates tests before code, followed by bounded, execution-driven refinement.
Wasay Mohammed Abdul, Ragib Shahariar Ayon, Shibbir Ahmed et al.· SIGSOFT FSE Companion· 0 citations
This paper introduces SpecCoder, a verification-guided CodeLLM training framework that learns from validated reference programs, behavior-changing mutants, and multi-turn specification-refinement traces, and improves checkpoint-specification quality over base CodeLLMs, and introduces HumanExec, a benchmark built from r...
Minh Le-Anh, Cuong Chi Le, Tien N. Nguyen· 0 citations
BeSpec is presented, a behavioral model-based approach to specification alignment that treats the task description as partial evidence about the intended behavior of the correct program, and first builds an explicit behavioral model, which are checkable properties that valid outputs must satisfy.
Qinghua Xu, Guancheng Wang, Boxi Yu et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.