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We propose CodeQA, a free-form question answering dataset for the purpose ofsource code comprehension: given a code snippet and a question, a textualanswer is required to be generated. CodeQA contains a Java dataset with 119,778question-answer pairs and a Python dataset with 70,085 question-answer pairs.To obtain natural and faithful questions and answers, we implement syntacticrules and semantic analysis to transform code comments into question-answerpairs. We present the construction process and conduct systematic analysis ofour dataset. Experiment results achieved by several neural baselines on ourdataset are shown and discussed. While research on question-answering andmachine reading comprehension develops rapidly, few prior work has drawnattention to code question answering. This new dataset can serve as a usefulresearch benchmark for source code comprehension.

CodeQA: A Question Answering Dataset for Source Code Comprehension