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Diversity driven adaptive test generation for concurrent data structures
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DOI：10.1016/j.infsof.2018.07.001.png)
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Abstract
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Context Testing concurrent data structures remains a notoriously challenging task, due to the nondeterminism of multi-threaded tests and the exponential explosion on the number of thread schedules. Objective: We propose an automated approach to generate a series of concurrent test cases in an adaptive manner, i.e., the next test cases are generated with the guarantee to discover the thread schedules that have not yet been activated by the previous test cases. Method Two diversity metrics are presented to induce such adaptive test cases from a static and a dynamic perspective, respectively. The static metric enforces the diversity in the program structures of the test cases; while the dynamic one enforces the diversity in their capabilities of exposing untested thread schedules. We implement three adaptive test generation approaches for C/C + + concurrent data structures, based on the stateof-the-art active testing engine Maple. Results: We then report an empirical study with 9 real-world C/C + + concurrent data structures, which demonstrates the efficiency of our test generation approaches in terms of the number of thread schedules discovered, as well as the time and the number of tests required for testing a concurrent data structure. Conclusion: Hence, by using diverse test cases derived from the static and dynamic perspectives, our adaptive test generation approaches can deliver a more efficient coverage of the thread schedules of the concurrent data structure under test.
Keywords:
Concurrent data structures
Test case diversity
Test case generation
Active testing
Adaptive random testing
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