Before revision:  They both meet the following drawbacks: 1) it is shown ineffective to incorporate SBT into Transformer; 2) it is limited to capture global information through GNN ; 3) it is underestimated to capture structural semantics only using Transformer. In this paper , we propose a novel model based on structure-induced self-attention , which encodes sequential inputs with highly-effective structure modeling .
After revision:  However, it is surprising that incorporating SBT into advanced encoder like Transformer instead of LSTM has been shown no performance gain, which lets GNN become the only rest means modeling such necessary structural clue in source code. To release such inconvenience , we propose a novel model based on structure-induced self-attention , which encodes sequential inputs with highly-effective structure modeling .