Dataflow analysis for concurrent programs using datarace detection

Chugh, Ravi; Voung, Jan W.; Jhala, Ranjit; Lerner, Sorin

doi:10.1145/1375581.1375620

Cited by 44 publications

(12 citation statements)

References 34 publications

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“…Chugh et al [9] demonstrate the effectiveness of conservative analysis; we will make similar determinations in our work.…”

Section: Related Worksupporting

confidence: 67%

“…Knoop et al introduce a framework that generalizes sequential static unidirectional bit-vector analyses to work with explicitly annotated parallel regions [18]. Chugh et al [9] propose a framework that first generates a static non-null analysis and later uses data race detection to kill facts that parallelism no longer guarantees to be true.…”

Section: Related Workmentioning

confidence: 99%

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Butterfly analysis

Goodstein

Vlachos

Chen

et al. 2010

SIGARCH Comput. Archit. News

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Online program monitoring is an effective technique for detecting bugs and security attacks in running applications. Extending these tools to monitor parallel programs is challenging because the tools must account for inter-thread dependences and relaxed memory consistency models. Existing tools assume sequential consistency and often slow down the monitored program by orders of magnitude. In this paper, we present a novel approach that avoids these pitfalls by not relying on strong consistency models or detailed inter-thread dependence tracking. Instead, we only assume that events in the distant past on all threads have become visible; we make no assumptions on (and avoid the overheads of tracking) the relative ordering of more recent events on other threads. To overcome the potential state explosion of considering all the possible orderings among recent events, we adapt two techniques from static dataflow analysis, reaching definitions and reaching expressions, to this new domain of dynamic parallel monitoring. Significant modifications to these techniques are proposed to ensure the correctness and efficiency of our approach. We show how our adapted analysis can be used in two popular memory and security tools. We prove that our approach does not miss errors, and sacrifices precision only due to the lack of a relative ordering among recent events. Moreover, our simulation study on a collection of Splash-2 and Parsec 2.0 benchmarks running a memory-checking tool on a hardware-assisted logging platform demonstrates the potential benefits in trading off a very low false positive rate for (i) reduced overhead and (ii) the ability to run on relaxed consistency models.

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“…Chugh et al [9] demonstrate the effectiveness of conservative analysis; we will make similar determinations in our work.…”

Section: Related Worksupporting

confidence: 67%

Section: Related Workmentioning

confidence: 99%

Butterfly analysis

Goodstein

Vlachos

Chen

et al. 2010

SIGARCH Comput. Archit. News

View full text Add to dashboard Cite

show abstract

“…Therefore, in order to solve this problem, further method need to introduce constraint solver to reduce the path does not reach. The second is, since this thesis is proposed to improve the standard UD and DU algorithm, there is no In-depth analysis of the dataflow with complex path [16]. And the false negatives are hardly to determine.…”

Section: Discussionmentioning

confidence: 99%

UAF bug checking model based on dataflow analysis

Ouyang

Wang

Wei

et al. 2010

2010 IEEE International Conference on Information Theory and Information Security

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Although there are some tools supporting to detect the program's use-after-free vulnerability, their performance may be degraded because of the restriction they have. In order to detect the program's use-after-free vulnerability with a relatively higher efficiency, in this paper, we propose an automated approach for checking the use-after-free vulnerability in the application. First, we deliberate and choose the method of analyzing the dataflow statically. Then we track all definition and usage for the variables in the application. Finally, the techniques of the equivalent variable and alias analysis are introduced. We have implemented the approach of this thesis in a tool called UAFChecker which can detect useafter-free vulnerability automatically and have conducted experiments with several real-life case studies, experimental results show that the tool can detect the defects of actual application with low false positives and negatives probability.

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“…Static analysis is not sensitive to interleaving. However, even with recent inspiring progress [7], its scalability and effectiveness in concurrent programs are still limited by the fundamental pointer alias and concurrency analysis problems. ConMem combines classic memory bug detection techniques with predictive interleaving analysis and interleaving testing, thus solving the above problems (more discussion is in Section 8).…”

Section: Contributionsmentioning

confidence: 99%

“…A recent study [7] inventively proposes leveraging race detection to improve data flow analysis in concurrent programs. The idea is very inspiring.…”

Section: Concurrent Programs' Empirical Studymentioning

confidence: 99%

ConMem

Zhang

Sun

2010

SIGPLAN Not.

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Multicore technology is making concurrent programs increasingly pervasive. Unfortunately, it is difficult to deliver reliable concurrent programs, because of the huge and non-deterministic interleaving space. In reality, without the resources to thoroughly check the interleaving space, critical concurrency bugs can slip into production runs and cause failures in the field. Approaches to making the best use of the limited resources and exposing severe concurrency bugs before software release would be desirable.Unlike previous work that focuses on bugs caused by specific interleavings (e.g., races and atomicity-violations), this paper targets concurrency bugs that result in one type of severe effects: program crashes. Our study of the error-propagation process of realworld concurrency bugs reveals a common pattern (50% in our non-deadlock concurrency bug set) that is highly correlated with program crashes. We call this pattern concurrency-memory bugs: buggy interleavings directly cause memory bugs (NULL-pointerdereference, dangling-pointer, buffer-overflow, uninitialized-read) on shared memory objects.Guided by this study, we built ConMem to monitor program execution, analyze memory accesses and synchronizations, and predicatively detect these common and severe concurrency-memory bugs. We also built a validator ConMem-v to automatically prune false positives by enforcing potential bug-triggering interleavings.We evaluated ConMem using 7 open-source programs with 9 real-world severe concurrency bugs. ConMem detects more tested bugs (8 out of 9 bugs) than a lock-set-based race detector and an unserializable-interleaving detector that detect 4 and 5 bugs respectively, with a false positive rate about one tenth of the compared tools. ConMem-v further prunes out all the false positives. Con-Mem has reasonable overhead suitable for development usage.

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Dataflow analysis for concurrent programs using datarace detection

Cited by 44 publications

References 34 publications

Butterfly analysis

Butterfly analysis

UAF bug checking model based on dataflow analysis

ConMem

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