nagini

dundant terms, and tends to make the resulting code both more human-readable, and faster to execute. > The above two techniques, optimization and normalization, allow MOSES to outperform standard genetic programming systems. opencog/asmoses: https://github.com/opencog/asmoses MOSES outputs Combo (a LISP), Python as an output transform IIUC, and now Atomese with asmoses, which links to a demo notebook: https://robert-haas.github.io/mevis-docs/code/examples/moses... Evolutionary algorithm > Convergence: https://en.wikipedia.org/wiki/Evolutionary_algorithm#Converg... /? mujoco learning to walk [with evolutionary selection / RL Reinforcement Learning] https://www.google.com/search?q=mujoco+learning+to+walk&tbm=... ... Semgrep: https://en.wikipedia.org/wiki/Semgrep links to OWASP Source Code Analysis Tools: https://owasp.org/www-community/Source_Code_Analysis_Tools But what's static analysis or dynamic analysis source code analysis without Formal Verification? "Nagini: A Static Verifier for Python": https://pm.inf.ethz.ch/publications/EilersMueller18.pdf https://github.com/marcoeilers/nagini : > However, there is currently virtually no tool support for reasoning about Python programs beyond type safety. > We present Nagini, a sound verifier for statically-typed, concurrent Python programs. Nagini can prove memory safety, data race freedom, and user-supplied assertions. Nagini performs modular verification, which is important for verifi- cation to scale and to be able to verify libraries, and automates the verification process for programs annotated with specifications. Deal > Formal verification > Background; Hoare logic, DbC Design by Contract, Dafny, Z3: https://deal.readthedocs.io/basic/verification.html#backgrou... : > 2021. deal-solver. We released a tool that converts Python code (including deal contracts) into Z3 theorems that can be formally verified.