SOURCE-LINKED INTELLIGENCE

MaxSAT-Based Misbehaviour Verification and Localisation Framework for Python

CORDIS · observation · Publication date unknown

gh Maximum Satisfiability (MaxSAT), to identify faulty program statements, has shown strong potential for languages such as C, but remains under-explored for Python, which is the dominant language in artificial intelligence, data science, and education. The aim of this project is to design and implement Sherlock4Py, a MaxSAT-based misbehaviour verification and localisation framework for Python. First, Sherlock4Py will implement FBFL to Python by incorporating bounded model checking with ESBMC-Python, and will develop scalable MaxSAT algorithms tailored to program-derived formulas. These algorithms will exploit structural features of Python, such as loop iterations and branching constructs, to achieve greater scalability and more precise diagnoses than existing approaches. Second, Sherlock4Py will demonstrate the synergy between FBFL and Large Language Models (LLMs) by embedding MaxSAT-based bug localisation into counterexample-guided inductive synthesis (CEGIS) loops, thereby enabling LLMs to synthesise and repair faulty Python programs more reliably, with greater accuracy and effi

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recordType

award

status

SIGNED

region

EU

value

194074.56

unit

EUR