Michael Tsautschnig, Amazon

Automating Software Analysis at Large Scale


Software model checking tools promise to deliver genuine traces to errors, and sometimes even proofs of their absence. As static analysers, they do not require concrete execution of programs, which may be even more beneficial when targeting new platforms. Academic research focusses on improving scalability, yet largely disregards practical technical challenges to make tools cope with real-world code. At Amazon, both scalability requirements as well as real-world constraints apply. Our prior work analysing more than 25,000 software packages in the Debian/GNU Linux distribution containing more than 400 million lines of C code not only led to more than 700 public bug reports, but also provided a solid preparation for the challenges at Amazon.


Michael Tautschnig is a Research Scientist at Amazon Web Services and lecturer at Queen Mary University of London. He obtained his PhD from Vienna University of Technology developing an efficient, systematic, and automatic test input generation technique for C programs. Prior to his appointment as lecturer he was a post-doctoral research assistant at the University of Oxford. A year ago he also joined Byron Cook's group at Amazon Web Services to apply his research at scale. His current and recent research focusses automating verification for real-world software at large scale, and efficient software verification techniques for concurrent, shared-memory systems with relaxed memory models.