ANaConDA: A Framework for Analysing Multi-threaded C/C++ Programs on the Binary Level

Fiedor, Jan; Vojnar, Tomáš

doi:10.1007/978-3-642-35632-2_5

Cited by 7 publications

(6 citation statements)

References 6 publications

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“…The goal of random noise injection techniques [8,[13][14][15] is to disturb the normal thread interleaving by randomly delaying threads using the sleep, yield, and busy waiting mechanisms to expose hidden concurrency bugs. IBM's ConTest [8] is a framework used to statically inject noise into a specific Java bytecode or sourcecode location.…”

Section: Random Noise Injectionmentioning

confidence: 99%

Efficient noise injection for exposing hidden data races

Bae

2019

J Supercomput

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The happens-before (HB) relation is widely used to precisely detect data races, which are among the major causes of concurrency bugs. However, the HB relation is sensitive to thread interleaving; thus, hidden races that can be revealed in other thread interleavings may remain despite the repeated running of a program with an HB detector. In this paper, we propose a noise injection-based race detection technique (RaceInducer) that can expose and detect hidden races by using the runtime HB analysis result to determine the noise injection instants. RaceInducer delays a thread execution after the occurrence of the first no-lock non-racing accesses of three types (read-exclusive, write-exclusive, and read-shared) to a shared variable during a precise HB detection (FastTrack). Using this technique, RaceInducer can efficiently disturb the normal locking sequences between two threads that generate HB edges hiding data races. We implemented RaceInducer on the RoadRunner instrumentation framework for Java and evaluated its detection capability and runtime overheads on 12 real-world benchmark programs. The evaluation results showed that RaceInducer generated a lower runtime overhead compared to random noise injection that randomly injected noise for no-lock non-racing accesses and can detect more races with the same noise strength. RaceInducer generates particularly lower runtime overhead than the random noise injection for large programs that generates a high number of shared-memory accesses. In addition, we experimentally demonstrated that RaceInducer incurred lower runtime overheads than a current hidden-race detector (DrFinder) for programs with a relatively large number of locking operations.

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Section: Random Noise Injectionmentioning

confidence: 99%

Efficient noise injection for exposing hidden data races

Bae

2019

J Supercomput

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“…They are referred as test cases t01 to t14. The ANaConDA framework was used to perform the tests. The framework uses the Intel PIN framework for dynamic binary instrumentation to insert the code implementing the noise‐injection heuristics into a C/C++ program binary.…”

Section: Noise‐injection Techniquesmentioning

confidence: 99%

“…The framework ANaConDA already mentioned in Section 4.3 was used to perform the tests. For each execution of a test, the framework collects information about test duration and about the fact whether an error is manifested.…”

Section: Noise‐injection Techniquesmentioning

confidence: 99%

“…It is also less precise as the compiler might, for example, move the code elsewhere because of some optimizations. Therefore, the ANaConDA framework used for the C/C++ programmes and the IBM ConTest framework used for the Java programmes insert the noise‐injection code on the binary and bytecode levels, respectively. In this section, a short summary of the experiences with implementing the noise‐injection techniques on the binary level of C/C++ code and the Java bytecode level is presented.…”

Section: Noise‐injection Techniquesmentioning

confidence: 99%

“…In this paper, a unified overview of multiple results from the area of noise‐injection‐based testing that were published by the authors in several past years is provided. In particular, multiple novel coverage metrics (first proposed by Křena et al ), which measure how well the behaviour of tested programmes has been examined from the point of view of possible occurrence of certain synchronization‐related errors, are presented first.…”

Section: Introductionmentioning

confidence: 99%

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Advances in noise‐based testing of concurrent software

Fiedor

Hrubá

Křena

et al. 2014

Software Testing Verif & Rel

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SummaryTesting of concurrent software written in programming languages like Java and C/C++ is a highly challenging task owing to the many possible interactions among threads. A simple, cheap, and effective approach that addresses this challenge is testing with noise injection, which influences the scheduling so that different interleavings of concurrent actions are witnessed. In this paper, multiple results achieved recently in the area of noise‐injection‐based testing by the authors are presented in a unified and extended way. In particular, various concurrency coverage metrics are presented first. Then, multiple heuristics for solving the noise placement problem (i.e. where and when to generate noise) as well as the noise seeding problem (i.e. how to generate the noise) are introduced and experimentally evaluated. In addition, several new heuristics are proposed and included into the evaluation too. Recommendations on how to set up noise‐based testing for particular scenarios are then given. Finally, a novel use of the genetic algorithm for finding suitable combinations of the many parameters of tests and noise techniques is presented. Copyright © 2014 John Wiley & Sons, Ltd.

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