[pl-seminar] talk today: statistical software debugging

[Ben Liblit <liblit@xxxxxxxxxxx>]

Today's AI seminar may be of interest to PL people as well:

--- Wednesday, July 5 ---
3:00 pm, 4310 CS (Cookies: 2:30 pm, 4310 CS)
Artificial Intelligence Seminar:
Alice Zheng, Carnegie Mellon University

"Statistical Software Debugging"

Traditional software debugging is an arduous task that requires time, 
effort, and a good understanding of the source code.  Given the scale 
and complexity of the task, the development of methods for automatically 
debugging software seems both essential and very difficult.  However, 
several trends make such an endeavor increasingly realistic: (1) the 
wide-scale deployment of software, (2) the establishment of distributed 
crash report feedback systems, and (3) the development of statistical 
machine learning algorithms that can take advantage of aggregate data 
over multiple users.
In this talk, I present a statistical software debugging framework that 
applies machine learning techniques to run-time reports of instrumented 
programs.  The problem has a relatively simple solution under the 
single-bug assumption.  However, in the more realistic case of multiple 
bugs, the problem can no longer be dealt with using feature selection 
and classification techniques.  I describe the challenges and present a 
solution inspired by bi-clustering algorithms.
This is joint work with Ben Liblit (U. Wisconsin, Madison), Michael 
Jordan (U.C. Berkeley), Alex Aiken and Mayur Naik (Stanford).