Automated detection of software vulnerabilities is a fundamental problem in software
security. Existing program analysis techniques either suffer from high false positives or false …

Many security and software testing applications require checking whether certain properties
of a program hold for any possible usage scenario. For instance, a tool for identifying …

This paper provides a comprehensive survey of techniques for testing machine learning
systems; Machine Learning Testing (ML testing) research. It covers 144 papers on testing …

Recently, hybrid fuzzing has been proposed to address the limitations of fuzzing and
concolic execution by combining both approaches. The hybrid approach has shown its …

Among the many software testing techniques available today, fuzzing has remained highly
popular due to its conceptual simplicity, its low barrier to deployment, and its vast amount of …

We present VerX, the first automated verifier able to prove functional properties of Ethereum
smart contracts. VerX addresses an important problem as all real-world contracts must …

Neural networks are difficult to interpret and debug. We introduce testing techniques for
neural networks that can discover errors occurring only for rare inputs. Specifically, we …

In recent years, fuzz testing has proven itself to be one of the most effective techniques for
finding correctness bugs and security vulnerabilities in practice. One particular fuzz testing …

Fuzzing is a simple yet effective approach to discover software bugs utilizing randomly
generated inputs. However, it is limited by coverage and cannot find bugs hidden in deep …

Testing involves examining the behaviour of a system in order to discover potential faults.
Given an input for a system, the challenge of distinguishing the corresponding desired …