Efficient Mutation-Analysis Coverage for Constrained Random Verification

Xie, Tao; Mueller, Wolfgang; Letombe, Florian

doi:10.1007/978-3-642-15234-4_12

Cited by 2 publications

(6 citation statements)

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“…For this, we compared an implementation of the search procedure in Figure 3 with the self-adjusting, constrained random test generator presented in [19]. This is at least enough to show the search effectiveness and other referenced implementations seem unavailable to us.…”

Section: Resultsmentioning

confidence: 98%

“…As the constrained random tests generator [19] does not target each individual fault but the whole fault set, it is reasonable that we at first apply this ultralow-cost generator to trim those easy-to-uncover mutation faults. This is also quite similar to the gate-level fault detection.…”

Section: Resultsmentioning

confidence: 99%

“…It can be used to complement a general fault-detection phase like those in [19] and [20]. A cost function for directing search heuristics has been defined on the test input space.…”

Section: Discussionmentioning

confidence: 99%

“…By utilizing the correlation between mutation faults, heuristics as [19] and [20] can efficiently achieve a relatively high degree of mutation coverage. Nevertheless, as also shown in their experimental data, a few stubborn, hard-to-detect faults will still be left after some reasonable effort.…”

Section: Fault Based Test Data Generationmentioning

confidence: 99%

“…not aiming at any individual fault. Examples include [19] and [20]. The former is a constraint-based random method, which utilizes the intermediate results of fault detection to steer the distribution of its tests generator on-the-fly, in order to achieve a higher efficiency.…”

Section: Fault Based Test Data Generationmentioning

confidence: 99%

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HDL-Mutation Based Simulation Data Generation by Propagation Guided Search

Xie

Mueller

Letombe³

2011

2011 14th Euromicro Conference on Digital System Design

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HDL-mutation based fault injection and analysis is considered as an important coverage metric for measuring the quality of design simulation processes [20, 3, 1, 2]. In this work, we try to solve the problem of automatic simulation data generation targeting HDL mutation faults. We follow a search based approach and eliminate the need for symbolic execution and mathematical constraint solving from existing work. An objective cost function is defined on the test input space and serves the guidance of search for fault-detecting test data. This is done by first mapping the simulation traces under a test onto a control and data flow graph structure which is extracted from the design. Then the progress of fault detection can be measured quantitatively on this graph to be the cost value. By minimizing this cost we approach the target test data. The effectiveness of the cost function is investigated under an example neighborhood search scheme. Case study with a floating point arithmetic IP design has shown that the cost function is able to guide effectively the search procedure towards a fault-detecting test. The cost calculation time as the search overhead was also observed to be minor compared to the actual design simulation time.

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Section: Resultsmentioning

confidence: 98%

Section: Resultsmentioning

confidence: 99%

“…It can be used to complement a general fault-detection phase like those in [19] and [20]. A cost function for directing search heuristics has been defined on the test input space.…”

Section: Discussionmentioning

confidence: 99%

Section: Fault Based Test Data Generationmentioning

confidence: 99%

Section: Fault Based Test Data Generationmentioning

confidence: 99%

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