Campus Event Calendar
Event Entry
What and Who
Exposing Concurrency Bugs from their Hiding Places
Umang Mathur
National University of Singapore
SWS Colloquium
Umang Mathur is a Presidential Young Professor at the National University
of Singapore. He received his PhD from the University of Illinois at
Urbana Champaign and was an NTT Research Fellow at the Simons Institute
for the Theory of Computing at Berkeley. His research broadly centers on
developing techniques inspired from formal methods and logic for answering
design, analysis and implementation questions in programming languages,
software engineering and systems. He has received a Google PhD Fellowship,
an ACM SIGSOFT Distinguished Paper Award at ESEC/FSE'18. Best Paper Award
at ASPLOS'22 and an ACM SIGPLAN Distinguished Paper Award at POPL'23 for
his work on designing techniques and tools for analyzing concurrent
software. More details can be found at:
https://www.comp.nus.edu.sg/~umathur/
AG 1, AG 2, AG 3, INET, AG 4, AG 5, D6, SWS, RG1, MMCI
AG Audience
English
Note: We use this to send email in the morning.
Date, Time and Location
Thursday, 26 October 2023
10:30
60 Minutes
G26
111
Kaiserslautern
Abstract
Concurrent programs are notoriously hard to write correctly, as scheduling
nondeterminism introduces subtle errors that are both hard to detect and
to reproduce.
Despite rigorous testing, concurrency bugs such as races conditions often
find their way into production software, and manifest as critical security
issues. Consequently, considerable effort has been made towards developing
efficient techniques for detecting them automatically.
The preferred approach to detect data races is through dynamic analysis,
where one executes the software with some test inputs, and checks for the
presence of bugs in the execution observed.
Traditional bug detectors however are often unable to discover simple bugs
present in the underlying software, even after executing the program
several times, because these bugs are sensitive to thread scheduling.
In this talk, I will discuss how runtime predictive analysis can help.
Runtime predictive analyses aim to expose concurrency bugs, that can be
otherwise missed by traditional dynamic analysis techniques (such as the
race detector TSan), by inferring the presence of these bugs in alternate
executions of the underlying software, without explicitly re-executing the
software program.
I will talk about the fundamentals of and recent algorithmic advances for
building highly scalable and sound predictive analysis techniques.
nondeterminism introduces subtle errors that are both hard to detect and
to reproduce.
Despite rigorous testing, concurrency bugs such as races conditions often
find their way into production software, and manifest as critical security
issues. Consequently, considerable effort has been made towards developing
efficient techniques for detecting them automatically.
The preferred approach to detect data races is through dynamic analysis,
where one executes the software with some test inputs, and checks for the
presence of bugs in the execution observed.
Traditional bug detectors however are often unable to discover simple bugs
present in the underlying software, even after executing the program
several times, because these bugs are sensitive to thread scheduling.
In this talk, I will discuss how runtime predictive analysis can help.
Runtime predictive analyses aim to expose concurrency bugs, that can be
otherwise missed by traditional dynamic analysis techniques (such as the
race detector TSan), by inferring the presence of these bugs in alternate
executions of the underlying software, without explicitly re-executing the
software program.
I will talk about the fundamentals of and recent algorithmic advances for
building highly scalable and sound predictive analysis techniques.
Contact
Susanne Girard
+49 631 9303 9605
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Video Broadcast
Yes
Saarbrücken
E1 5
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Susanne Girard, 10/19/2023 09:30 -- Created document.