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Distinguished Speaker Series Winter/Spring 2010

Mary Lou Soffa

Mary Lou Soffa

Department Chair
Owens R. Cheatham Professor of Science
Department of Computer Science
University of Virginia



Path-Based Fault Correlations

March 5, 2010

Refreshments and Networking: 1:30 - 2:00
Presentation: 2:00 - 3:30

Faculty Host: Prof. James A. (Jim) Jones, ISR


RSVP: Email RSVP required to Kiana Fallah by Monday March 1.

Location: Donald Bren Hall (building #314), room 6011

Cost: No cost to attend.

Directions and parking information
are available.


Abstract: Although a number of automatic tools have been introduced to detect faults, much of the diagnosis is still being done manually. To help with the diagnostic task, we introduce the concept of fault correlation, which is a causal relationship between faults. We statically determine correlations based on the expected dynamic behavior of a fault. If the occurrence of one fault causes another fault to occur, we say they are correlated. Finding correlations of faults has a number of advantages. With the identification of the correlated faults, we can better understand fault behaviors and the propagation of faults. If one fault is uniquely correlated with another, fixing the first fault will fix the other. Correlated faults can be grouped, enabling prioritization of diagnoses of the fault groups. In this paper,we develop an interprocedural, path-sensitive and scalable algorithm to automatically compute correlated faults in a program. In our approach, we first detect faults and determine their error states. By propagating the effects of the error state along a path, we detect the correlation of pairs of faults. We automatically construct a correlation graph which shows how correlations occur among multiple faults and along different paths. Using the correlation graph, the task of diagnosing root causes is guided by the information in the graph. We implemented our correlation algorithm and found through experimentation that fault correlations exist in real-world applications, and faults involved in the correlations can be of different types and located in different procedures. Using the correlation information, we are able to automate diagnostic tasks that previously had to be done manually.


About the Speaker: Mary Lou Soffa is the Owen T. Cheatham Professor of Sciences and Department Chair of the Computer Science Department at the University of Virginia. From 1977 to 2004, she was a Professor of Computer Science at the University of Pittsburgh and also served as the Dean of Graduate Studies in the College of Arts and Sciences from 1991 to 1996.

Her research interests include software tools for debugging and testing programs, virtual execution environments, optimizing compilers, and program analysis. She has published over 150 papers in journals and conferences. Her papers have received a number of best paper awards as well a designation of one of the 40 most influential papers in 20 years to appear in the Programming Language Design and Implementation Conference. She has directed 24 Ph.D. students to completion, half of whom are women. She also directed over 50 M.S. students, with half being women.

Soffa received the Nico Habermann Award in 2006 for outstanding contributions toward increasing the numbers and successes of underrepresented members in the computing research community. In 1999, she received the Presidential Award for Excellence in Science, Mathematics and Engineering Mentoring. She was elected an ACM Fellow in 1999 and selected as a Girl Scout Woman of Distinction in 2003. She served for ten years on the Board of the Computing Research Association (CRA) and continues as a member of CRA-W, the committee on the status of women in computer science and engineering of the CRA. She co-founded the CRA-W Graduate Cohort Program and the CRA-W Cohort for Associate Professors. She has served on the Executive Committees of both ACM SIGSOFT and SIGPLAN as well as conference chair, program chair or program committee member of many conferences. Currently, she is an ACM Council Member-at-Large and serves on the ACM Publications Board.