BCGen: a comment generation method for bytecode

Abstract

AbstractBytecode is a form of instruction set designed for efficient execution by a software interpreter. Unlike human-readable source code, bytecode is even harder to understand for programmers and researchers. Bytecode has been widely used in various software tasks such as malware detection and clone detection. In order to understand the meaning of the bytecode more quickly and accurately and further help programmers in more software activities, we propose a bytecode comment generation method (called BCGen) using neural language model. Specifically, to get the structured information of the bytecode, we first generate the control flow graph (CFG) of the bytecode, and serialize the CFG with bytecode semantic information. Then a transformer model combining gate recurrent unit is proposed to learn the features of bytecode to generate comments. We obtain the bytecode by building the Jar packages of the well-known open-source projects in the Maven repository and construct a bytecode dataset to train and evaluate our model. Experimental results show that the BLEU of BCGen can reach 0.26, which outperforms several baselines and proves the effectiveness and practicability of our method. It is concluded that it is possible to generate natural language comments directly from the bytecode. Meanwhile, it is important to take structured and semantic information into account in generating bytecode comments.