Lin Tan, PhD, P.Eng.
Associate Professor, Electrical and Computer Engineering, University of Waterloo
Her research focuses on improving software dependability, addressing the software bug problem at every stage of a bug’s life-cycle by creating techniques to avoid, predict, prevent and fix bugs. Her work reduces the cost of software development and the impact and frequency of bug-induced software failures such as crashes, breaches of security and performance degradation.
Dr. Tan has pioneered a new branch of research that leverages various forms of software text for software bug detection in source code and code comments. Her iComment and Document-Assisted Symbolic Execution (DASE) tools are innovations that enable dramatically more effective, accurate and efficient bug detection through automated analysis of code comments and documentation. She has cleverly extended these (and other) tools to analyze a broad range of software text types, topics and programming languages for a variety of new purposes.
Dr. Tan has built an impressive research portfolio, securing more than $1.3 million in research funding from provincial and federal sources, and substantial contributions from industry. Leading companies including Google and IBM are key partners in her research and also support internship and full-time jobs for many of her students, a testament to the outstanding training environment she has created. She has trained over 60 graduate and undergraduate students in the last six years, many of whom have held prestigious scholarships and awards.