Abstract Code comment generation, which attempts to summarize the functionality of source code in textual descriptions, plays an important role in automatic software development research. Currently, several structural neural networks have been exploited to preserve the syntax structure of source code based on abstract syntax trees (ASTs). However, they can not well capture both the long‐distance and local relations between nodes while retaining the overall structural information of AST. To mitigate this problem, we present a prototype tool titled PyScribe, which extends the Transformer model to a new encoder‐decoder‐based framework. Particularly, the triplet position is designed and integrated into the node‐level and edge‐level structural features of AST for producing Python code comments automatically. This paper, to the best of our knowledge, makes the first effort to model the edges of AST as an explicit component for improved code representation. By specifying triplet positions for each node and edge, the overall structural information can be well preserved in the learning process. Moreover, the captured node and edge features go through a two‐stage decoding process to yield higher qualified comments. To evaluate the effectiveness of PyScribe, we resort to a large dataset of code‐comment pairs by mining Jupyter Notebooks from GitHub, for which we have made it publicly available to support further studies. The experimental results reveal that PyScribe is indeed effective, outperforming the state‐ofthe‐art by achieving an average BLEU score (i.e., av‐BLEU) of 0.28.

Automatic code summarization, which aims to describe the source code in natural language, has become an essential task in software maintenance.Our fellow researchers have attempted to achieve such a purpose through various machine learning-based approaches.One key challenge keeping these approaches from being practical lies in the lacking of retaining the semantic structure of source code, which has unfortunately been overlooked by the stateof-the-art methods.Existing approaches resort to representing the syntax structure of code by modeling the Abstract Syntax Trees (ASTs).However, the hierarchical structures of ASTs have not been well explored.In this paper, we propose CODESCRIBE to model the hierarchical syntax structure of code by introducing a novel triplet position for code summarization.Specifically, CODESCRIBE leverages the graph neural network and Transformer to preserve the structural and sequential information of code, respectively.In addition, we propose a pointer-generator network that pays attention to both the structure and sequential tokens of code for a better summary generation.Experiments on two real-world datasets in Java and Python demonstrate the effectiveness of our proposed approach when compared with several state-of-the-art baselines 1 .