Using program induction for verification

Summary: I discussed before (e.g. here) how connecting rule-based verification to the rule-less, amorphous Machine Learning world is really hard, and yet necessary. The current post talks about a somewhat-exotic technique called Program Induction (PI), and how it might (eventually) help bridge that gap. What’s program induction Background: I always liked the idea of synthesizing … More Using program induction for verification

Dynamic verification in one picture

Summary: This post tries to summarize what dynamic verification is, using a single picture. It then puts various verification tools, and diverse verification projects, in the context of that picture. It also explains Coverage Driven Verification (CDV). The Foretellix blog is about verifying complex systems. However, as I discussed here, there is no agreed-upon verification … More Dynamic verification in one picture

What’s new in AV verification: Report from the Stuttgart symposium

Summary: This is part one of my report about what I saw at the Stuttgart 2017 Autonomous Vehicles test & development symposium last week. This yearly symposium seems to be a pretty good place to get the feeling of what’s going on in AV verification (at least in Europe): There are several AV-related conferences, but … More What’s new in AV verification: Report from the Stuttgart symposium

One-shot imitation learning and verification

Summary: This post will talk about “One-shot imitation learning” (a new and exciting direction in Machine Learning), and how that direction could help coverage maximization (which is important for verification). It will then speculate about the general role of ML in Intelligent Autonomous Systems verification. Note: You may have heard already about one-shot imitation learning … More One-shot imitation learning and verification

Verification, coverage and maximization: The big picture

Summary: This post tries to explain (once and for all?) how the concept of coverage is used to optimize the verification process, what it means to auto-maximize coverage, and how people have tried to do it. I have been spending some time lately on coverage maximization via ML (which I described here). As is often … More Verification, coverage and maximization: The big picture

Machine Learning for Coverage Maximization

Summary: This post describes in general terms the problem of “using ML for coverage maximization”, explains why it is important for CDV and for fuzzing, and gives some references. My first post in the “ML and verification” series talked about verifying ML-based systems (lots more to say about that). This post talks about the other … More Machine Learning for Coverage Maximization