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ASE 2019

CocoQa: Question Answering for Coding Conventions Over Knowledge Graphs

Tianjiao Du, Junming Cao, Qinyue Wu, Wei Li, Beijun Shen, Yuting Chen

DOI 10.1109/ase.2019.00108dblp

Abstract

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.

BibTeX
@inproceedings{Du-al:ASE19,
  author    = {Tianjiao Du and
               Junming Cao and
               Qinyue Wu and
               Wei Li and
               Beijun Shen and
               Yuting Chen},
  title     = {{CocoQa:} Question Answering for Coding Conventions Over Knowledge Graphs},
  booktitle = {ASE},
  pages     = {1086--1089},
  publisher = {{IEEE}},
  year      = {2019},
}

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