Fault localization and failure detection are important activities in assessing and maintaining the quality of software systems. Techniques to locate faults and identify failures typically require significant manual effort. Our techniques gather runtime information about the program's execution to automatically predict when failures are occurring and then to guide the developer in locating faults. The techniques use both static (compile-time) and dynamic (runtime) information about program, along with statistics and machine learning to accomplish the tasks.

In this talk, I will overview these techniques, place them in context with the rest of the software development process, discuss some scenarios of their usage, and provide some empirical evaluation of their effectiveness.

Bio

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Mary Jean Harrold is the NSF ADVANCE Professor of Computing at Georgia Tech. She performs research in analysis and testing of large, evolving software, fault-localization using statistical analysis and visualization, monitoring deployed software to improve quality, and software self-awareness through real-time assessment and response. Professor Harrold received an NSF NYI Award and was named an ACM Fellow. She serves on the editorial board of ACM TOPLAS and ACM TOSEM, on the Board of Directors for the Computing Research Association (CRA), as Vice Chair of ACM SIGSOFT, as co-chair of the CRA Committee on the Status of Women in Computing (CRA-W) and a member of the Leadership Team of the National Center for Women and Information Technology. She received the Ph.D. from the University of Pittsburgh.