The effectiveness of binary analysis tools and techniques is often measured with respect to how well they map to a ground truth. We have found that not all ground truths are created equal. This paper challenges the binary analysis community to take a long look at the concept of ground truth, to ensure that we are in agreement with definition(s) of ground truth, so that we can be confident in the evaluation of tools and techniques. This becomes even more important as we move to trained machine learning models, which are only as useful as the validity of the ground truth in the training.
翻译：衡量二元分析工具和技术的有效性时往往参照它们如何很好地描绘到地面真相,我们发现并非所有地面真相都是平等的,本文要求二元分析界长期审视地面真相的概念,以确保我们同意地面真相的定义,从而能够有信心地评价各种工具和技术,这在我们转向经过训练的机器学习模型时变得更加重要,这些模型与培训中实地真相的有效性一样有用。
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