CS 201 | Seongmin Lee, UCLA Comp Sci Dept

“The Magic of Statistics for Software Testing: How to Foresee the Unseen”

Ensuring software correctness is essential as software increasingly governs critical aspects of modern life, yet both formal verification and software testing face fundamental limitations. Testing, in particular, is inherently incomplete—it inevitably misses certain behaviors, leaving “unseen’’ program states and defects unaccounted for. This seminar introduces how statistical methods can help quantify these unseen, offering principled ways to assess what testing has not yet revealed. I will showcase recent advances that estimate residual risk, predict the reachability of critical behaviors, and extrapolate the future progress of fuzzing campaigns. By the end, participants will see how statistical thinking provides actionable insights that make testing more transparent, accountable, and empirically grounded.

Seongmin Lee is a postdoctoral researcher at UCLA, specializing in software testing, program analysis, and software security. His research develops statistical methods that make software analysis more scalable and reliable, with applications to fuzzing, reachability estimation, and information-flow analysis, and his work has appeared in top-tier software engineering and security venues such as ICSE, FSE, and S&P. His recent work also extends into machine learning, including a Spotlight Paper Award at ICLR 2025 for advances in estimating unseen behaviors. Before joining UCLA, he was a postdoctoral researcher at the Max Planck Institute for Security and Privacy (MPI-SP) in Germany, and he received his Ph.D. from KAIST in South Korea. He has also served on organizing and program committees for major software engineering venues, including SBST, ICSE, FSE, and ASE.

Date/Time:
Date(s) - Jan 13, 2026
4:00 pm - 5:45 pm

Location:
3400 Boelter Hall
420 Westwood Plaza Los Angeles California 90095