Doctoral Thesis: Automatic Patch Generation via Learning from Successful Human Patches

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Event Speaker: 

Fan Long

Event Location: 

32-D463 (Star)

Event Date/Time: 

Thursday, September 28, 2017 - 2:15pm

Abstract:
 
Software systems are increasingly integrated into every part of our society. As
the number of systems and our dependence on these systems continue to grow,
making these systems reliable and secure becomes an increasingly important
challenge for our society and a daunting task for software developers.

Automatic patch generation holds out the promise of automatically correcting
software defects without the need for developers to manually diagnose,
understand, and correct these defects. In this talk, I will present two novel
automatic patch generation systems, Prophet and Genesis, both of which learn
from past successful human patches to automatically fix defects. By
collectively leveraging development efforts worldwide, Prophet and Genesis
automatically generate correct patches for real-world defects in large
open-source C and Java applications with up to millions lines of code. This
research also demonstrates that the growing volume of software programs is not
just a challenge but also a great opportunity. Exploiting this opportunity can
enable revolutionary new automated techniques that enhance software reliability
and security.
 
Thesis Committee: 
Prof. Martin Rinard 
Prof. Arvind
Prof. Armando Solar-Lezama