The Inconvenient Truths of Ground Truth for Binary Analysis
This is an incremental call for standardization in binary analysis to improve evaluation reliability.
The paper addresses the problem of inconsistent ground truth definitions in binary analysis, which undermines tool evaluation and machine learning model training, by urging the community to standardize these definitions.
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.