Shin Hong is an assistant professor in the school of Computer Science and Electrical Engineering at Handong Global University. He received his PhD in computer science from KAIST in 2015, where he also earned his BS and MS. His Ph.D research provided systematic and practical testing methods for concurrent programs including multithreaded programs and event-driven programs. His research interest continues to develop automated software testing and techniques for complex software systems.
Despite significant advances in software engineering during the last half century, debugging remains as one of the most difficult, labor-intensive, tedious activity to date. Debugging demands that human developers acquire deep understanding of the target program and reason about complicated cause-effect relations among an enormous number of code entities in real-world software. Researchers had developed automated techniques that infers the location of a fault by statistically analyzing observed testing results, however, they could not achieve high accuracies for effective real-world applications. In this talk, I will present a new automated debugging approach called mutation-based fault localization (MBFL). The key idea of MBFL is to acquire useful debugging information by systematically applying mutations to the target programs. The case studies with real-world open-source projects will show how MBFL successfully assists developers debugging complicated real-world faults with multilingual features.