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

Wenbo Wang, Yi Li, Shaohua Wang, Xinyue Ye

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)2675-2700
Number of pages26
JournalTransactions in GIS
Issue number5
StatePublished - Oct 2021

All Science Journal Classification (ASJC) codes

  • General Earth and Planetary Sciences


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