2011 26th IEEE/ACM International Conference on Automated Software Engineering (ASE 2011) 2011
DOI: 10.1109/ase.2011.6100093
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Automatic generation of load tests

Abstract: Load tests aim to validate whether system performance is acceptable under peak conditions. Existing test generation techniques induce load by increasing the size or rate of the input. Ignoring the particular input values, however, may lead to test suites that grossly mischaracterize a system's performance. To address this limitation we introduce a mixed symbolic execution based approach that is unique in how it 1) favors program paths associated with a performance measure of interest, 2) operates in an iterati… Show more

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Cited by 60 publications
(44 citation statements)
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“…A Markov Model can be used to generate load test suites automatically [19,20,21,22,23]. Zhang presents a mixed symbolic execution approach aimed at discovering execution paths that contribute to high program loads while ensuring path diversity [24]. Malk presents a methodology to help automatically identify important performance counters for load testing and compare the counters across tests to find performance gain/loss [25].…”
Section: Detection Resultsmentioning
confidence: 99%
“…A Markov Model can be used to generate load test suites automatically [19,20,21,22,23]. Zhang presents a mixed symbolic execution approach aimed at discovering execution paths that contribute to high program loads while ensuring path diversity [24]. Malk presents a methodology to help automatically identify important performance counters for load testing and compare the counters across tests to find performance gain/loss [25].…”
Section: Detection Resultsmentioning
confidence: 99%
“…This way, a bug in the code that processes option -o will be more likely to be exposed with DASE. On the other hand, the probability of hitting a bug in a shallower option (e.g., b) would be reduced from 15 , but the difference is much smaller, and it is still highly likely that the option b will be tested given that the probability is 15 . In addition to finding more bugs, since each option has about 1 15 chance to be explored, more options are likely to be tested, improving testing coverage ( § VI-B shows that DASE covers more options than KLEE).…”
Section: Overviewmentioning
confidence: 99%
“…Symbolic execution [7], [8] has been leveraged to automatically generate high code coverage test suites to detect bugs [9]- [15]. Symbolic execution represents inputs as symbolic values instead of concrete values.…”
Section: Introductionmentioning
confidence: 99%
“…In contrast to these existing approaches, SpeedGun addresses the challenges of generating concurrent performance tests. Zhang et al propose mixed symbolic-concrete test generation that focuses on paths that are estimated to have a high performance impact [59]. Wise generates tests that expose the worst-case complexity of a program [8].…”
Section: Test Generationmentioning
confidence: 99%