# The Inconvenient Truths of Ground Truth for Binary Analysis

arXiv: https://arxiv.org/abs/2210.15079 · HTML: https://mlsanity.com/paper/2210.15079/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 26 Oct 2022.

## Builds on (most cited first, 3 of 3)

- [Revisiting Binary Code Similarity Analysis using Interpretable Feature Engineering and Lessons Learned](https://mlsanity.com/paper/2011.10749/) (2020)
- [SoK: All You Ever Wanted to Know About x86/x64 Binary Disassembly But Were Afraid to Ask](https://mlsanity.com/paper/2007.14266/) (2020)
- [On the Generation of Disassembly Ground Truth and the Evaluation of Disassemblers](https://mlsanity.com/paper/2012.09155/) (2020)

## Built on by (most cited first, 1 of 1)

- [A Deep Dive into Function Inlining and its Security Implications for ML-based Binary Analysis](https://mlsanity.com/paper/2512.14045/) (2025)

## Abstract (arXiv)

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.