ESBMC-Python is an innovative tool that uses formal verification techniques to ensure the correctness of Python programs by converting them into abstract syntax trees (ASTs) and then using an efficient bounded model checker framework. The practical solution and value of ESBMC-Python lie in its ability to verify Python programs efficiently, despite the language's dynamic nature and lack of explicit type information. It has demonstrated impressive performance in handling large codebases and extensive program sets, as well as identifying critical errors in real-world applications, such as a division-by-zero error in the Ethereum consensus specification. ESBMC-Python ensures the safety and correctness of Python programs, providing a valuable benchmark for future verification tools. It plans to extend its capabilities by including more features and enhancing the type inference algorithm to handle complex program flows. For free consultation and more information on AI solutions, you can visit the AI Lab in Telegram @itinai or follow on Twitter @itinaicom.
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