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Program comprehension is a fundamental task in software development andmaintenance processes. Software developers often need to understand a largeamount of existing code before they can develop new features or fix bugs inexisting programs. Being able to process programming language codeautomatically and provide summaries of code functionality accurately cansignificantly help developers to reduce time spent in code navigation andunderstanding, and thus increase productivity. Different from natural languagearticles, source code in programming languages often follows rigid syntacticalstructures and there can exist dependencies among code elements that arelocated far away from each other through complex control flows and data flows.Existing studies on tree-based convolutional neural networks (TBCNN) and gatedgraph neural networks (GGNN) are not able to capture essential semanticdependencies among code elements accurately. In this paper, we propose noveltree-based capsule networks (TreeCaps) and relevant techniques for processingprogram code in an automated way that encodes code syntactical structures andcaptures code dependencies more accurately. Based on evaluation on programswritten in different programming languages, we show that our TreeCaps-basedapproach can outperform other approaches in classifying the functionalities ofmany programs.

TreeCaps: Tree-Structured Capsule Networks for Program Source Code Processing