Bash command comment generation via multi-scale heterogeneous feature fusion

Abstract

Automatic generation of Bash command comments is crucial for understanding and updating commands in software maintenance. Existing mainstream methods mainly focus on learning from the sequential text of Bash commands and combining retrieval-enhanced techniques to generate comments. However, these methods overlook the syntactic structure of Bash commands, thereby limiting the quality and accuracy of generated comments. This paper proposes a heterogeneous Bash comment generation framework named HBCom, which is aimed at deeply exploring the semantic information of Bash commands from command token sequences and syntactic structures to generate more accurate and natural command comments. The core of HBCom lies in constructing a Heterogeneous Information Graph (HIG) based on an Abstract Syntax Tree, which integrates the syntactic structure of Bash commands with the code sequence through six types of edges, providing a solid information basis for subsequent comment generation. In addition, we propose a heterogeneous and multi-scale graph neural network to capture various relationships in HIGs. Subsequently, we utilize a Transformer decoder, combined with a copy mechanism based on multi-head attention, to decode and fuse the HIG and Bash command tokens features, ultimately generating high-quality comments. We conduct extensive experiments on Bash dataset, demonstrating that HBCom outperforms compared baseline models in BLEU, ROUGE-L, and METEOR metrics. Furthermore, human evaluations confirm HBCom’s effectiveness in real-world application scenarios.