Turn Tree into Graph: Automatic Code Review via Simplified AST Driven Graph Convolutional Network
B. Wu, B. Liang, X. Zhang

TL;DR
This paper introduces a new dataset and a graph convolutional network model that simplifies ASTs to improve automatic code review accuracy by capturing structural relations more effectively.
Contribution
It proposes a novel Simplified AST based Graph Convolutional Network (SimAST-GCN) and releases the first large-scale dataset for ACR, advancing research in automated code review.
Findings
SimAST-GCN outperforms existing methods on AACR dataset
Simplifying AST improves information transmission in code graphs
Graph-based approach effectively captures code structure for review tasks
Abstract
Automatic code review (ACR), which can relieve the costs of manual inspection, is an indispensable and essential task in software engineering. To deal with ACR, existing work is to serialize the abstract syntax tree (AST). However, making sense of the whole AST with sequence encoding approach is a daunting task, mostly due to some redundant nodes in AST hinder the transmission of node information. Not to mention that the serialized representation is inadequate to grasp the information of tree structure in AST. In this paper, we first present a new large-scale Apache Automatic Code Review (AACR) dataset for ACR task since there is still no publicly available dataset in this task. The release of this dataset would push forward the research in this field. Based on it, we propose a novel Simplified AST based Graph Convolutional Network (SimAST-GCN) to deal with ACR task. Concretely, to…
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Taxonomy
TopicsSoftware Engineering Research · Software Engineering Techniques and Practices · Software Testing and Debugging Techniques
