Fuzz testing (fuzzing) has witnessed its prosperity in detecting security flaws recently. It
generates a large number of test cases and monitors the executions for defects. Fuzzing has …

Highlights•Analysis of variability provides information about control priorities.•Variability
comprises a multitude of processes that contribute to skill improvement.•Stages of learning …

Program synthesis has been long studied with recent approaches focused on directly using
the power of Large Language Models (LLMs) to generate code. Programming benchmarks …

Large language models (LLMs) have recently experienced tremendous popularity and are
widely used from casual conversations to AI-driven programming. However, despite their …

Unit tests play a key role in ensuring the correctness of software. However, manually
creating unit tests is a laborious task, motivating the need for automation. Large Language …

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 …

Existing Greybox Fuzzers (GF) cannot be effectively directed, for instance, towards
problematic changes or patches, towards critical system calls or dangerous locations, or …

Abstract Machine learning is a powerful tool for gleaning knowledge from massive amounts
of data. While a great deal of machine learning research has focused on improving the …

As the ubiquity and complexity of system-on-chip (SoC) designs increase across electronic
devices, incorporating security into an SoC design flow poses significant challenges …

Coverage-based Greybox Fuzzing (CGF) is a random testing approach that requires no
program analysis. A new test is generated by slightly mutating a seed input. If the test …