Precise and maximal race detection from incomplete traces

Huang, Jeff; Rajagopalan, Arun

doi:10.1145/2983990.2984024

Cited by 27 publications

(20 citation statements)

References 49 publications

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“…Our SDP-and WDP-based approaches are slower than other predictive approaches, but they find more races, some of which are millions of events apart. SMT-based approaches would not be able to find these far-apart races because they cannot scale past analyzing bounded windows of executions [Chen et al 2008;Huang et al 2014;Huang and Rajagopalan 2016;Liu et al 2016;Said et al 2011;Şerbănuţă et al 2013] (Section 8). Notably, RVPredict, which (like WDP) incorporates precise control and data dependence, uses an analysis window of 10,000 events [Huang et al 2014], meaning it would miss many of the predictable races detected and verified by our approach.…”

Section: Summary and Discussionmentioning

confidence: 99%

“…Race characteristics. SMT-solver-based predictive race detectors can be as precise as SDP and WDP analyses, but cannot scale to unbounded program executions [Chen et al 2008;Huang et al 2014;Huang and Rajagopalan 2016;Liu et al 2016;Said et al 2011;Şerbănuţă et al 2013] (Section 8). These approaches typically analyze bounded windows of an execution trace, missing races involving łfar apartž events.…”

Section: Race Detection Effectivenessmentioning

confidence: 99%

“…A promising alternative to HB analysis is sound predictive analysis, which detects additional predictable data races from an observed execution [Chen et al 2008;Huang et al 2014;Huang and Rajagopalan 2016;Kini et al 2017;Liu et al 2016;Pavlogiannis 2019;Roemer et al 2018;Said et al 2011;Şerbănuţă et al 2013;Smaragdakis et al 2012]; an analysis is sound if it detects no false races (Section 2). Some predictive analyses rely on generating and solving SMT constraints, so in practice they cannot scale to full program executions and instead analyze bounded windows of execution, missing races between accesses that do not execute close together [Chen et al 2008;Huang et al 2014;Huang and Rajagopalan 2016;Liu et al 2016;Said et al 2011;Şerbănuţă et al 2013] (Section 8). In contrast, unbounded predictive analyses avoid this limitation by detecting races based on computing a partial order weaker than HB, using analyses with linear running time in the length of the trace [Kini et al 2017;Roemer et al 2018].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dependence-aware, unbounded sound predictive race detection

Genç

Roemer

Yu-fan

et al. 2019

Proc. ACM Program. Lang.

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Data races are a real problem for parallel software, yet hard to detect. Sound predictive analysis observes a program execution and detects data races that exist in some other, unobserved execution. However, existing predictive analyses miss races because they do not scale to full program executions or do not precisely incorporate data and control dependence. This paper introduces two novel, sound predictive approaches that incorporate data and control dependence and handle full program executions. An evaluation using real, large Java programs shows that these approaches detect more data races than the closest related approaches, thus advancing the state of the art in sound predictive race detection. CCS Concepts: • Software and its engineering → Dynamic analysis; Software testing and debugging.

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Section: Summary and Discussionmentioning

confidence: 99%

Section: Race Detection Effectivenessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dependence-aware, unbounded sound predictive race detection

Genç

Roemer

Yu-fan

et al. 2019

Proc. ACM Program. Lang.

View full text Add to dashboard Cite

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“…Other techniques can be categorized as predictive and can detect races missed by HB by exploring more correct reorderings of an observed trace. These include use of SMT solvers [Huang et al 2014;Huang and Rajagopalan 2016;Liu et al 2016;Said et al 2011] and other techniques based on weakening the HB partial order including CP [Smaragdakis et al 2012] and WCP [Kini et al 2017]. Amongst these, WCP is the only technique that has a linear running time and is known to scale to large traces.…”

Section: Related Workmentioning

confidence: 99%

What happens-after the first race? enhancing the predictive power of happens-before based dynamic race detection

Mathur

Kini

Viswanathan

2018

Proc. ACM Program. Lang.

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Dynamic race detection is the problem of determining if an observed program execution reveals the presence of a data race in a program. The classical approach to solving this problem is to detect if there is a pair of conflicting memory accesses that are unordered by Lamport's happens-before (HB) relation. HB based race detection is known to not report false positives, i.e., it is sound. However, the soundness guarantee of HB only promises that the first pair of unordered, conflicting events is a schedulable data race. That is, there can be pairs of HB-unordered conflicting data accesses that are not schedulable races because there is no reordering of the events of the execution, where the events in race can be executed immediately after each other. We introduce a new partial order, called schedulable happens-before (SHB) that exactly characterizes the pairs of schedulable data races -every pair of conflicting data accesses that are identified by SHB can be scheduled, and every HB-race that can be scheduled is identified by SHB. Thus, the SHB partial order is truly sound. We present a linear time, vector clock algorithm to detect schedulable races using SHB. Our experiments demonstrate the value of our algorithm for dynamic race detection -SHB incurs only little performance overhead and can scale to executions from real-world software applications without compromising soundness.

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“…These methods explore all possible interleavings that can be deduced from the given trace, but such complete explorations are known to be intractable. RVPredict [34] has also been extended for analyzing traces with missing events [16]. Predictive analysis techniques have also been used for checking atomicity violations and synchronization errors in tools like jPredictor [6], [38] and TAME [17].…”

Section: Related Workmentioning

confidence: 99%

Dynamic race prediction in linear time

2017

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Writing reliable concurrent software remains a huge challenge for today's programmers. Programmers rarely reason about their code by explicitly considering different possible inter-leavings of its execution. We consider the problem of detecting data races from individual executions in a sound manner. The classical approach to solving this problem has been to use Lamport's happens-before (HB) relation. Until now HB remains the only approach that runs in linear time. Previous efforts in improving over HB such as causallyprecedes (CP) and maximal causal models fall short due to the fact that they are not implementable efficiently and hence have to compromise on their race detecting ability by limiting their techniques to bounded sized fragments of the execution. We present a new relation weak-causally-precedes (WCP) that is provably better than CP in terms of being able to detect more races, while still remaining sound. Moreover it admits a linear time algorithm which works on the entire execution without having to fragment it.

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Precise and maximal race detection from incomplete traces

Cited by 27 publications

References 49 publications

Dependence-aware, unbounded sound predictive race detection

Dependence-aware, unbounded sound predictive race detection

What happens-after the first race? enhancing the predictive power of happens-before based dynamic race detection

Dynamic race prediction in linear time

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