Autonomous testing complements conventional testing by leveraging cheap compute to explore software state spaces and uncover “unknown unknowns” beyond human-written tests. It spans a spectrum from random-input fuzzing, which is fast but struggles with complex conditions, to symbolic execution, which uses SAT solvers to systematically reach hard-to-hit paths—though these solvers can become prohibitively slow on complex constraints. Exe strikes a balance through concolic execution: it runs bare-metal code on concrete inputs while instrumenting paths with logical constraints, invoking a solver only when needed to explore alternate branches. This approach combines the speed of concrete execution with the path-finding power of symbolic methods, avoiding the full cost of traditional symbolic engines.
talk-data.com
M
Speaker
Michael Vaughn
1
talks
Senior Software Engineer
Antithesis
Michael Vaughn holds a PhD in computer science from the University of Wisconsin–Madison and is a senior software engineer at Antithesis, working on their hypervisor and fuzzer. He has spent years researching at the intersection of operating systems and programming languages, often writing large amounts of x86 assembly, C, Scheme, Haskell, and LaTeX—sometimes in the same day. He has also worked as a pub trivia host and enjoys board games, hiking, and reading.
Bio from: Michael Vaughn on EXE: Automatically Generating Inputs of Death
Filter by Event / Source
Talks & appearances
1 activities · Newest first