Visible to the public SCMiner: Localizing System-Level Concurrency Faults from Large System Call Traces

TitleSCMiner: Localizing System-Level Concurrency Faults from Large System Call Traces
Publication TypeConference Paper
Year of Publication2019
AuthorsZaman, Tarannum Shaila, Han, Xue, Yu, Tingting
Conference Name2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)
Date PublishedNov. 2019
ISBN Number978-1-7281-2508-4
Keywordscomposability, Computer bugs, Concurrency, concurrency (computers), Concurrency Failures, Concurrent computing, CPS, cyber physical systems, data mining, Debugging, default system audit tools, event handlers, failure-inducing system call sequences, fault diagnosis, fault localization, file content, Instruments, interleaving schedule, localizing system-level concurrency faults, Metrics, Multi Process Applications, multiple failing executions, multiple processes, practical online bug diagnosis tool, principal component analysis, Production, production system, program debugging, program diagnostics, program testing, pubcrawl, resilience, Resiliency, scheduling, SCMiner, security, security of data, statistical analysis, statistical anomaly detection techniques, system call traces, System Level, system-level concurrency fault, Tools

Localizing concurrency faults that occur in production is hard because, (1) detailed field data, such as user input, file content and interleaving schedule, may not be available to developers to reproduce the failure; (2) it is often impractical to assume the availability of multiple failing executions to localize the faults using existing techniques; (3) it is challenging to search for buggy locations in an application given limited runtime data; and, (4) concurrency failures at the system level often involve multiple processes or event handlers (e.g., software signals), which can not be handled by existing tools for diagnosing intra-process(thread-level) failures. To address these problems, we present SCMiner, a practical online bug diagnosis tool to help developers understand how a system-level concurrency fault happens based on the logs collected by the default system audit tools. SCMiner achieves online bug diagnosis to obviate the need for offline bug reproduction. SCMiner does not require code instrumentation on the production system or rely on the assumption of the availability of multiple failing executions. Specifically, after the system call traces are collected, SCMiner uses data mining and statistical anomaly detection techniques to identify the failure-inducing system call sequences. It then maps each abnormal sequence to specific application functions. We have conducted an empirical study on 19 real-world benchmarks. The results show that SCMiner is both effective and efficient at localizing system-level concurrency faults.

Citation Keyzaman_scminer_2019