Improving Software Reliability through Decoupled Dynamic Analysis

Instruction-by-instruction checking of program execution is a powerful
method for identifying and mitigating hard-to-find software bugs,
including security vulnerabilities. However, adoption of this technique
has been limited by the associated performance overheads. This talk will
show that decoupling the analysis from, and running it concurrently with
the monitored program is a promising approach for addressing the performance
limitations of heavyweight program monitoring. Using system software
monitoring, I will show that decoupling enables arbitrary instruction-grained
dynamic analysis of kernel-mode drivers for safeguarding persistent I/O device
state from corruption by driver bugs, without incurring slowdowns that could
break timing-sensitive interrupt handling codes. I will present three novel
tools for data races, DMA bugs and memory bugs in drivers that are enabled by
decoupling. Using application software monitoring, I will show that decoupling
enables further optimizations of hand-tuned instruction-grained dynamic analysis
codes. I will present novel compiler-based and parallelism-based dynamic optimization
techniques that individually achieve up to 3X speedup of state-of-the-art tools for
mitigating data races, memory bugs and security vulnerabilities in applications.

Olatunji Ruwase is a PhD candidate in the Computer Science Department at
Carnegie Mellon University, where he explores compilers, computer architecture,
and operating systems techniques for addressing performance and correctness
issues in computing systems. He is a recipient of the 2010 Intel PhD Fellowship,
and holds a MS in Computer Science from Stanford University and a BS in Computer
Science from University of Ibadan.