ELAINE WEYUKER
AT&T Labs Researcher
Fault Prediction - Goals, Models, Experience
ABSTRACT: It would obviously be very valuable to know in advance which files in the next release of a large software system are most likely to contain the largest numbers of faults. To accomplish this, we developed a negative binomial regression model and used it to predict the expected number of faults in each file of the next release of a system. The predictions are based on code characteristics and fault and modification history data. I will discuss what we have learned from applying the model to four large industrial systems, each with multiple years of field exposure, and tell you about our success in making accurate predictions and some of the lessons learned and issues that had to be dealt with.
BIO: Elaine Weyuker is a researcher at AT&T Labs who specializes in empirical software engineering and testing research. She is a member of the National Academy of Engineering, an IEEE Fellow, an ACM Fellow and an AT&T Fellow. She is the chair of the ACM Committee on Women in Computing (ACM-W) and a member of the Coalition to Diversify Computing's Executive Committee. She was the 2004 recipient of the IEEE Computer Society's Harlan D. Mills Award for distinguished research, the Rutgers University 50th Anniversary Outstanding Alumni Award, and the AT&T Chairman's Diversity Award. Before moving to AT&T, she was a computer science professor at the Courant Institute of Mathematical Sciences of NYU.
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