Optimization problems in the power market - to verify or not to verify

Learning to Verify Systems

Making high quality and reliable software systems remains a difficult problem. One approach to address this problem is automated verification which attempts to demonstrate algorithmically that a software system meets its specification. However, verification of software systems is not easy: such systems are often modeled using abstractions of infinite structures such as unbounded integers, infin...

Learning to Verify the Heap

We present a data-driven verification framework to automatically prove memory safety and functional correctness of heap programs. For this, we introduce a novel statistical machine learning technique that maps observed program states to (possibly disjunctive) separation logic formulas describing the invariant shape of data structures at relevant program locations. We then attempt to verify thes...

Learning to Verify Safety Properties

We present a novel approach for verifying safety properties of finite state machines communicating over unbounded FIFO channels that is based on applying machine learning techniques. We assume that we are given a model of the system and learn the set of reachable states from a sample set of executions of the system, instead of attempting to iteratively compute the reachable states. The learnt s...

What Makes Petri Nets Harder to Verify: Stack or Data?

We show how the yardstick construction of Stockmeyer, also developed as counter bootstrapping by Lipton, can be adapted and extended to obtain new lower bounds for the coverability problem for two prominent classes of systems based on Petri nets: Ackermann-hardness for unordered data Petri nets, and Tower-hardness for pushdown vector addition systems.

Scan your patient to verify surgical site.