A coverage driven test generation methodology using consistency algorithm

George, M. P. Jomu; Mohamed, Otmane Aït

doi:10.1109/asqed.2013.6643559

Cited by 1 publication

(1 citation statement)

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“…Katz et al proposed to learn test knowledge from micro-architectural behavior and embed the knowledge into test generator to produce more effective tests [13]. In [14], the authors proposed a methodology based on consistency algorithm to attain faster coverage. While many promising techniques have been proposed, CDTG remains an on-going and active research area [15].…”

Section: Coverage-directed Test Generation (Cdtg) Techniques Dynamicamentioning

confidence: 99%

Improving the efficiency of functional verification based on test prioritization

Wang

Huang

2016

Microprocessors and Microsystems

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Functional verification has become the key bottleneck that delays time-to-market during the embedded system design process. And simulation-based verification is the mainstream practice in functional verification due to its flexibility and scalability. In practice, the success of the simulation-based verification highly depends on the quality of functional tests in use which is usually evaluated by coverage metrics. Since test prioritization can provide a way to simulate the more important tests which can improve the coverage metrics evidently earlier, we propose a test prioritization approach based on the clustering algorithm to obtain a high coverage level earlier in the simulation process. The k-means algorithm, which is one of the most popular clustering algorithms and usually used for the test prioritization, has some shortcomings which have an effect on the effectiveness of test prioritization. Thus we propose three enhanced k-means algorithms to overcome these shortcomings and improve the effectiveness of the test prioritization. Then the functional tests in the simulation environment can be ordered with the test prioritization based on the enhanced k-means algorithms. Finally, the more important tests, which can improve the coverage metrics evidently, can be selected and simulated early within the limited simulation time. Experimental results show that the enhanced k-means algorithms are more accurate and efficient than the standard k-means algorithm for the test prioritization, especially the third enhanced k-means algorithm. In comparison with simulating all the tests randomly, the more important tests, which are selected with the test prioritization based on the third enhanced k-means algorithm, achieve almost the same coverage metrics in a shorter time, which achieves a 90% simulation time saving.

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Section: Coverage-directed Test Generation (Cdtg) Techniques Dynamicamentioning

confidence: 99%