CodeQA: A Question Answering Dataset for Source Code Comprehension
Chenxiao Liu, Xiaojun Wan · 2021
We propose CodeQA, a free-form question answering dataset for the purpose of source code comprehension: given a code snippet and a question, a textual answer is required to be generated.CodeQA contains a Java dataset with 119,778 question-answer pairs and a Python dataset with 70,085 question-answer pairs.To obtain natural and faithful questions and answers, we implement syntactic rules and semantic analysis to transform code comments into question-answer pairs.We present the construction process and conduct systematic analysis of our dataset.Experiment results achieved by several neural baselines on our dataset are shown and discussed.While research on question-answering and machine reading comprehension develops rapidly, few prior work has drawn attention to code question answering.This new dataset can serve as a useful research benchmark for source code comprehension.