Cross Platform API Mappings based on API Documentation Graphs

2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2022-12-01 DOI:10.1109/QRS57517.2022.00097

Yanjie Shao, Tianyue Luo, Xiang Ling, Limin Wang, Senwen Zheng

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Abstract

As different versions of the same application might be implemented based on different platforms/programming languages, it is significantly important to build an automated migration tool for the application programming interface (API) mapping relations between different platforms/programming languages. In this paper, we propose an approach to discover API mappings based on the API documentation. We first divide the information in the API documentation into different types of entities, relations, and attributes to construct their respective API Documentation Graphs (ADGs). Then, we encode nodes, edges and triplets of ADGs and input them to a new graph neural network (GNN) for entity alignment to obtain the API mappings between the two different platforms/programming languages. Taking HarmonyOS and Android as representative cases, we evaluate our approach based on their API documentation. The results show that our approach improves top-1, top-5, and top10 accuracies by 50.57%, 56.25%, and 52.66%, respectively, compared with documentation-based baselines.

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基于API文档图的跨平台API映射

由于同一应用程序的不同版本可能基于不同的平台/编程语言实现，因此为不同平台/编程语言之间映射关系的应用程序编程接口(API)构建自动化迁移工具非常重要。在本文中，我们提出了一种基于API文档发现API映射的方法。我们首先将API文档中的信息划分为不同类型的实体、关系和属性，以构建各自的API文档图(API documentation graph, adg)。然后，我们对adg的节点、边和三元组进行编码，并将其输入到一个新的图神经网络(GNN)中进行实体对齐，从而获得两种不同平台/编程语言之间的API映射。以HarmonyOS和Android为例，我们根据它们的API文档来评估我们的方法。结果表明，与基于文档的基线相比，我们的方法将top-1、top-5和top10的准确率分别提高了50.57%、56.25%和52.66%。

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2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS)

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