QA4GIS: A novel approach learning to answer GIS developer questions with API documentation

Document Type

Article

Publication Date

10-1-2021

Abstract

Community-based question answering websites have attracted more and more scholars and developers to discuss domain knowledge and software development. In this article, we focus on the GIS section of the Stack Exchange website and develop a novel approach, QA4GIS, a deep learning-based system for question answering tasks with a deep neural network (DNN) model to extract the representation of the query–API document pair. We use the LambdaMART model to rerank the candidate API documents. We begin with an empirical analysis of the questions and answers, demonstrating that API documents could answer 52.93% of the questions. Then we evaluate QA4GIS by comparing it with 10 other baselines. The experiment results show that QA4GIS can improve 21.39% on the MAP score and 22.34% on the MRR score compared with the best baseline SIF.

Identifier

85110425806 (Scopus)

Publication Title

Transactions in GIS

External Full Text Location

https://doi.org/10.1111/tgis.12798

e-ISSN

14679671

ISSN

13611682

First Page

2675

Last Page

2700

Issue

5

Volume

25

Grant

1937908

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