Surprise Adequacy (SA) is one of the emerging and most promising adequacy criteria for Deep Learning (DL) testing. As an adequacy criterion, it has been used to assess the strength of DL test suites. In addition, it has also been used to find inputs to a Deep Neural Network (DNN) which were not sufficiently represented in the training data, or to select samples for DNN retraining. However, computation of the SA metric for a test suite can be prohibitively expensive, as it involves a quadratic number of distance calculations. Hence, we developed and released a performance-optimized, but functionally equivalent, implementation of SA, reducing the evaluation time by up to 97\%. We also propose refined variants of the SA omputation algorithm, aiming to further increase the evaluation speed. We then performed an empirical study on MNIST, focused on the out-of-distribution detection capabilities of SA, which allowed us to reproduce parts of the results presented when SA was first released. The experiments show that our refined variants are substantially faster than plain SA, while producing comparable outcomes. Our experimental results exposed also an overlooked issue of SA: it can be highly sensitive to the non-determinism associated with the DNN training procedure.
翻译:惊喜充足性(SA)是深海学习(DL)测试的新兴和最有希望的适当标准之一。作为一种适足性标准,它被用来评估DL测试套件的强度。此外,它还被用来为深神经网络寻找培训数据中没有充分体现的输入,或为DNN再培训挑选样本。然而,为测试套件计算SA指标可能过于昂贵,因为它涉及一段四级距离计算。因此,我们制定并发布了一个性能优化但功能等同的SA执行标准,将评估时间缩短到97个。我们还提出了SA光化算法的精细变体,以进一步提高评价速度。我们随后对MNIST进行了一项经验研究,重点是SA的离销检测能力,使我们得以复制在SA首次发布时提出的部分结果。实验表明,我们精细化的变体比比普通SA快得多,同时产生可比较的结果。我们的实验结果还暴露了SA的被忽视的SA:它与SA-ND-期的训练过程是高度敏感的。