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DTEND:20191211T120000Z
UID:4b8373d21d1aed7053f062ae458bd364-19
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DESCRIPTION:Optimistic Hybrid Analysis for System Security and Reliability
URL;VALUE=URI:https://www.csa.iisc.ac.in/newweb/event/19/optimistic-hybrid-analysis-for-system-security-and-reliability/
SUMMARY:Dynamic analysis tools such as information-flow tracking (DIFT) and data-race detection are useful for enforcing security policies and improving software reliability. But these tools are rarely used in production systems, as it can slow down a program by an order of magnitude. Static whole program analyses can be used to prove safe execution states and elide unnecessary runtime checks, but in practice, they are mostly ineffective for large programs. The reason is that they are greatly hindered by the need to prove their soundness, as soundness requires analysis of all possible executions and sound over-approximations of a program. 
This talk presents Optimistic Hybrid Analysis (OHA).  OHA improves the scalability and precision of whole program static analysis by one to two orders of magnitude by making optimistic assumptions about a program’s properties that are almost always true, but are hard to prove statically. By making these assumptions, we sacrifice soundness of static analysis, but yet, we preserve soundness of dynamic analysis by checking them at runtime and recovering when they fail. 
OHA has been used to obtain three promising results. It speeds up FastTrack, a well-known dynamic data-race detector by 3.5x; reduces the overhead of DIFT to less than 10%, a 4.4x improvement; enables the first known solution for a sound garbage collector for C/C++ using efficient pointer provenance.
DTSTART:20191211T120000Z
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