Abstract

Nowadays, data science software and notably machine learning plays an increasingly important role in our daily lives and it is slowly creeping into safety-critical scenarios. It thus becomes of paramount importance to have effective verification tools available to prove its correctness and reliability. In this talk, I will give a lightweight introduction to static analysis-based verification and I will discuss some key challenges and research directions that I am currently addressing in developing static analysis methods and tools targeting data science software.