一句话：CocoQa 通过知识图谱子图匹配与神经网络机器阅读理解，精确回答编码规范问题。
编码规范对保证软件质量很重要，但通常以非正式形式呈现，程序员不便使用。本文提出 CocoQa 系统，通过查询编码规范知识图谱来回答程序员关于编码规范的问题。CocoQa 采用两种技术：1）子图匹配算法，将自然语言问题解析为 SPARQL 查询；2）机器阅读理解算法，使用端到端神经网络从检索到的段落中检测答案。作者实现了 CocoQa，并在编码规范问答数据集上进行评估。结果表明，CocoQa 能精确回答编码规范问题，达到 82.92% 的精确率和 91.10% 的召回率。
Coding convention plays an important role in guaranteeing software quality. However, coding conventions are usually informally presented and inconvenient for programmers to use. In this paper, we present CocoQa, a system that answers programmer's questions about coding conventions. CocoQa answers questions by querying a knowledge graph for coding conventions. It employs 1) a subgraph matching algorithm that parses the question into a SPARQL query, and 2) a machine comprehension algorithm that uses an end-to-end neural network to detect answers from searched paragraphs. We have implemented CocoQa, and evaluated it on a coding convention QA dataset. The results show that CocoQa can answer questions about coding conventions precisely. In particular, CocoQa can achieve a precision of 82.92% and a recall of 91.10%. Repository: https://github.com/14dtj/CocoQa/ Video: https://youtu.be/VQaXi1WydAU.
DOI 原文 ·
@inproceedings{paperbot5321,
title = {CocoQa: Question Answering for Coding Conventions Over Knowledge Graphs},
author = {Tianjiao Du and Junming Cao and Qinyue Wu and Wei Li and Beijun Shen and Yuting Chen},
booktitle = {ASE},
year = {2019},
doi = {10.1109/ase.2019.00108}
}