A Look Back on a Function Identification Problem

A function recognition problem serves as a basis for further binary analysis and many applications. Although common challenges for function detection are well known, prior works have repeatedly claimed a noticeable result with a high precision and recall. In this paper, we aim to fill the void of what has been overlooked or misinterpreted by closely looking into the previous datasets, metrics, and evaluations with varying case studies. Our major findings are that i) a common corpus like GNU utilities is insufficient to represent the effectiveness of function identification, ii) it is difficult to claim, at least in the current form, that an ML-oriented approach is scientifically superior to deterministic ones like IDA or Ghidra, iii) the current metrics may not be reasonable enough to measure varying function detection cases, and iv) the capability of recognizing functions depends on each tool's strategic or peculiar choice. We perform re-evaluation of existing approaches on our own dataset, demonstrating that not a single state-of-the-art tool dominates all the others. In conclusion, a function detection problem has not yet been fully addressed, and we need a better methodology and metric to make advances in the field of function identification.