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Pat: A Pattern Classification Approach To Automatic Reference Oracles For The Testing Of Mesh Simplification Programs

机译:Pat:用于网格简化程序测试的自动参考Oracle的模式分类方法

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Graphics applications often need to manipulate numerous graphical objects stored as polygonal models. Mesh simplification is an approach to vary the levels of visual details as appropriate, thereby improving on the overall performance of the applications. Different mesh simplification algorithms may cater for different needs, producing diversified types of simplified polygonal model as a result. Testing mesh simplification implementations is essential to assure the quality of the graphics applications. However, it is very difficult to determine the oracles (or expected outcomes) of mesh simplification for the verification of test results.rnA reference model is an implementation closely related to the program under test. Is it possible to use such reference models as pseudo-oracles for testing mesh simplification programs? If so, how effective are they?rnThis paper presents a fault-based pattern classification methodology, called PAT, to address the questions. In PAT, we train the C4.5 classifier using black-box features of samples from a reference model and its fault-based versions, in order to test samples from the subject program. We evaluate PAT using four implementations of mesh simplification algorithms as reference models applied to 44 open-source three-dimensional polygonal models. Empirical results reveal that the use of a reference model as a pseudo-oracle is effective for testing the implementations of resembling mesh simplification algorithms. However, the results also show a tradeoff: When compared with a simple reference model, the use of a resembling but sophisticated reference model is more effective and accurate but less robust.
机译:图形应用程序经常需要操纵大量存储为多边形模型的图形对象。网格简化是一种适当更改视觉细节级别的方法,从而改善了应用程序的整体性能。不同的网格简化算法可以满足不同的需求,从而产生多种类型的简化多边形模型。测试网格简化实现对于确保图形应用程序的质量至关重要。但是,确定网格简化的预言(或预期结果)以验证测试结果非常困难。参考模型是与被测程序密切相关的实现。是否可以使用诸如伪预言之类的参考模型来测试网格简化程序?如果是这样,它们是否有效?本文提出了一种基于故障的模式分类方法,称为PAT,以解决这些问题。在PAT中,我们使用参考模型及其基于故障的版本的样本的黑盒特征训练C4.5分类器,以便测试主题程序中的样本。我们使用网格简化算法的四种实现方式评估PAT,将其作为参考模型应用于44个开源三维多边形模型。实验结果表明,将参考模型用作伪预言对于测试类似网格简化算法的实现是有效的。但是,结果也显示出一个权衡:与简单的参考模型相比,使用相似但复杂的参考模型更有效,更准确,但鲁棒性更低。

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