Generating Unit Tests for Concurrent Classes

Steenbuck, Sebastian

doi:10.1109/icst.2013.33

Cited by 30 publications

(16 citation statements)

References 25 publications

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“…Terragni and Pezzà survey several works on test generation for concurrent objects [45]. Like Violat, Ballerina [31] and ConTeGe [33] enumerate tests randomly, while ConSuite [43], AutoConTest [44], and CovCon [6] exploit static analysis to compute potential shared-memory access conflicts to reduce redundancy among generated tests. Similarly, Omen [35][36][37][38], Narada [40], Intruder [39], and Minion [41] reduce redundancy by anticipating potential concurrency faults during sequential execution.…”

Section: Related Workmentioning

confidence: 99%

Violat: Generating Tests of Observational Refinement for Concurrent Objects

Emmi

Enea

2019

Computer Aided Verification

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High-performance multithreaded software often relies on optimized implementations of common abstract data types (ADTs) like counters, key-value stores, and queues, i.e., concurrent objects. By using fine-grained and non-blocking mechanisms for efficient inter-thread synchronization, these implementations are vulnerable to violations of ADTconsistency which are difficult to detect: bugs can depend on specific combinations of method invocations and argument values, as well as rarely-occurring thread interleavings. Even given a bug-triggering interleaving, detection generally requires unintuitive test assertions to capture inconsistent combinations of invocation return values. In this work we describe the Violat tool for generating tests that witness violations to atomicity, or weaker consistency properties. Violat generates self-contained and efficient programs that test observational refinement, i.e., substitutability of a given ADT with a given implementation. Our approach is both sound and complete in the limit: for every consistency violation there is a failed execution of some test program, and every failed test signals an actual consistency violation. In practice we compromise soundness for efficiency via random exploration of test programs, yielding probabilistic soundness instead. Violat's tests reliably expose ADT-consistency violations using off-the-shelf approaches to concurrent test validation, including stress testing and explicit-state model checking.

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Section: Related Workmentioning

confidence: 99%

Violat: Generating Tests of Observational Refinement for Concurrent Objects

Emmi

Enea

2019

Computer Aided Verification

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“…Recently, an application of meta‐heuristics—in particular, the genetic algorithm—to the problem of unit test generation has been presented . The technique produces a test suite (i.e.…”

Section: Meta‐heuristic Setting Of Test and Noise Parametersmentioning

confidence: 99%

“…The earlier idea of a fully automatic generation of new unit tests based on a suitable combination of selected sequential programmes seems to be promising. The deterministic approach of building schedules can be replaced by heuristic noise injection as well. However, as already mentioned in Section 2, deterministic testing brings important benefits over noise injection when scalability is not an issue, which is usually the case for unit testing.…”

Section: Meta‐heuristic Setting Of Test and Noise Parametersmentioning

confidence: 99%

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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“…Recent work extends these ideas to multi-threaded test generation [41,37,48]. In contrast to these existing approaches, SpeedGun addresses the challenges of generating concurrent performance tests.…”

Section: Test Generationmentioning

confidence: 99%

Performance regression testing of concurrent classes

Pradel

Huggler

Groß

2014

Proceedings of the 2014 International Symposium on Software Testing and Analysis

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Developers of thread-safe classes struggle with two opposing goals. The class must be correct, which requires synchronizing concurrent accesses, and the class should provide reasonable performance, which is difficult to realize in the presence of unnecessary synchronization. Validating the performance of a thread-safe class is challenging because it requires diverse workloads that use the class, because existing performance analysis techniques focus on individual bottleneck methods, and because reliably measuring the performance of concurrent executions is difficult. This paper presents SpeedGun, an automatic performance regression testing technique for thread-safe classes. The key idea is to generate multi-threaded performance tests and to compare two versions of a class with each other. The analysis notifies developers when changing a thread-safe class significantly influences the performance of clients of this class. An evaluation with 113 pairs of classes from popular Java projects shows that the analysis effectively identifies 13 performance differences, including performance regressions that the respective developers were not aware of.

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Generating Unit Tests for Concurrent Classes

Cited by 30 publications

References 25 publications

Violat: Generating Tests of Observational Refinement for Concurrent Objects

Violat: Generating Tests of Observational Refinement for Concurrent Objects

Advances in noise‐based testing of concurrent software

Performance regression testing of concurrent classes

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