Habib Saissi, Stefan Winter, Oliver Schwan, Karthik Pattabiraman, and Neeraj Suri, IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 2020. (Acceptance Rate: 16.5%). [PDF | Talk] (Code)
Abstract: Modern computing systems typically relax execution determinism, for instance by allowing the CPU scheduler to inter-leave the execution of several threads. While beneficial for performance, execution non-determinism affects programs’ execution traces and hampers the comparability of repeated executions. We present TraceSanitizer, a novel approach for execution trace comparison in Error Propagation Analyses (EPA) of multi-threaded programs. TraceSanitizer can identify and compensate for non-determinism caused either by dynamic memory allocation or by non-deterministic scheduling. We formulate a condition under which TraceSanitizer is guaranteed to achieve a 0% false positive rate and automate its verification using Satisfiability Modulo Theory (SMT) solving techniques. TraceSanitizer is comprehensively evaluated using execution traces from the PARSEC and Phoenix benchmarks. In contrast with other approaches, TraceSanitizer eliminates false positives without increasing the false negative rate (for a specific class of programs), with reasonable performance overheads.
-
Recent Papers
- A Low-cost Strategic Monitoring Approach for Scalable and Interpretable Error Detection in Deep Neural Networks
- SwarmFuzz: Discovering GPS Spoofing Attacks in Drone Swarms
- AChecker: Statically Detecting Smart Contract Access Control Vulnerabilities
- Jujutsu: A Two-stage Defense against Adversarial Patch Attacks on Deep Neural Networks
- A Large-scale Empirical Study of Low-level Function Use in Ethereum Smart Contracts and Automated Replacement
- Characterizing Variability in Heterogeneous Edge Systems: A Methodology & Case Study
- LLTFI: Framework Agnostic Fault Injection for Machine Learning Applications (Tools and Artifact Track)
- Fault Injection for TensorFlow Applications
- eTainter: Detecting Gas-Related Vulnerabilities in Smart Contracts
- The Fault in Our Data Stars: Studying Mitigation Techniques against Faulty Training Data in ML Applications
Pages