Informatics Seminar Series
Winter Quarter 2022

Friday, February 25, 2022

“Why Isn't The Bug Apocalypse Coming Yet? *Seminar Cancelled*”

Gail Kaiser
Professor of Computer Science
Columbia University

Major news outlets have reported that a 'bug apocalypse' is coming, but unfortunately for people dependent on software directly or indirectly - which is pretty much everyone - they mean insects. We've been finding and fixing software bugs for 75 years, counting from Grace Hopper's moth, so why isn't a 'software bug apocalypse' coming yet – or here?

There are many fine researchers in formal methods, programming languages, and other areas trying to eliminate software bugs at the source, the developers' mistakes, and I hope they are successful. In the meantime, I work in program analysis and software testing: trying to find bugs that fallible developers have created, so they can be fixed. But finding complicated bugs in modern software is very hard. I will talk about some challenges and work by myself and others in the community trying to meet those challenges.

Gail E. Kaiser is a Professor of Computer Science in the Computer Science Department at Columbia University. Prof. Kaiser conducts research in software engineering and security from a systems perspective, focusing on program analysis and software testing. In the 1980s and 1990s, Kaiser investigated semantics-focused extensions to language-based editors and process-oriented team software development environments, forerunners to today's IDEs and Continuous Integration, and in the late 1990s and early 2000s she investigated self-adaptation for the then-emerging cloud computing, particularly techniques for retrofitting legacy systems. Since then she has concentrated on testing and analysis, often working at the bytecode/binary level. Beginning with her sabbatical at Columbia's Center for Computational Learning Systems in 2005-2006, Kaiser and her former PhD student Chris Murphy were among the first to adapt software engineering testing techniques, particularly metamorphic testing, to finding bugs in machine learning software. In recent years her work in program analysis ranges across static and dynamic techniques, across source code and executable (bytecode/binaries) targets, and investigates AI4SE as well as SE4AI. Prof. Kaiser received her PhD from CMU and her ScB from MIT.

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