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 defects. To accomplish this, we developed a negative binomial regression model and used it to predict the expected number of defects in each file of the next release of a system. The predictions are based on code characteristics and fault and modification history data. We will discuss what we have learned from applying the model to several 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 an AT&T Fellow doing software engineering research. Prior to moving to AT&T she was a professor of computer science at NYU's Courant Institute of Mathematical Sciences. Her research interests currently focus on software fault prediction, software testing, and software metrics and measurement. In an earlier life, Elaine did research in Theory of Computation and is the co-author of a book "Computability, Complexity, and Languages" with Martin Davis and Ron Sigal.

Elaine is the recipient of the 2008 Anita Borg Institute Technical Leadership Award and 2007 ACM/SIGSOFT Outstanding Research Award. She is also a member of the US National Academy of Engineering, an IEEE Fellow, and an ACM Fellow and has received IEEE's Harlan Mills Award for outstanding software engineering research, Rutgers University 50th Anniversary Outstanding Alumni Award, and the AT&T Chairman's Diversity Award as well has having been named a Woman of Achievement by the YWCA. She is the chair of the ACM Women's Council (ACM-W) and a member of the Executive Committee of the Coalition to Diversify Computing.

Reception immediately following the talk
2nd Floor Mezzanine Level
Levin Hall