EECS Special Seminar: Saikat Dutta, "Randomness-Aware Testing of Machine Learning-based Systems"


Saikat Dutta
University of Illinois Urbana-Champaign


Martin Rinard
Abstract: The goal of my research is to develop novel testing techniques and tools to make Machine Learning-based systems more reliable. Machine Learning is rapidly revolutionizing the way modern-day systems are developed. However, testing Machine Learning-based systems is challenging due to 1) the presence of non-determinism, both internal (e.g., stochastic algorithms) and external (e.g., execution environment), and 2) the absence of well-defined accuracy specifications. Most traditional software testing techniques widely used today cannot tackle these challenges because they often assume determinism and require a precise test oracle.

In this talk, I will present my work on automated testing of Machine Learning-based systems and on improving developer-written tests in such systems. To achieve these goals, I develop principled techniques that build on solid mathematical foundations from probability theory and statistics to reason about the underlying non-determinism and accuracy. I implement my techniques in practical and scalable tools that help developers to detect more bugs and to efficiently navigate trade-offs between test quality and efficiency. To date, my research has exposed more than 50 bugs and improved the quality of more than 200 tests in over 60 popular open-source ML libraries, many of which are widely used at companies like DeepMind, Google, Meta, Microsoft, and Uber as well as in many academic and scientific communities.

Biography: Saikat Dutta is a PhD candidate in Computer Science at the University of Illinois Urbana-Champaign, advised by Prof. Sasa Misailovic. Saikat’s research interests lie at the intersection of Software Engineering and Machine Learning. Saikat’s current research focuses on improving the reliability of Machine-learning based systems by developing novel testing techniques and tools. Saikat has received the Facebook PhD Fellowship, the 3M Foundation Fellowship, and the Mavis Future Faculty Fellowship for his contributions.