In Nomine Function: Naming Functions in Stripped Binaries with Neural Networks
Table 3: Results and comparison with DEBIN on DEBIN-Test subset containing 48k functions.
['[EMPTY]', 'Prec.', 'Rec.', 'f1']
We can see that all solutions are markedly better than the RANDOM strategy. On this dataset the Transformer outperforms the other solutions reaching an f1-score of 0.194, beating DEBIN 0.046 and Seq2Seq 0.108. Variable names reuse common patterns (as an example fd,file,f are standard variable names for files), and this could justify an higher rating than the one obtained by looking only at their performances on function names.