CS1QA: A Dataset for Assisting Code-based Question Answering in an Introductory Programming Course
This dataset provides a benchmark for source code comprehension and question answering in educational settings, addressing a domain-specific problem for programming instructors and AI researchers.
The authors introduced CS1QA, a dataset of 9,237 question-answer pairs from an introductory programming course, to assist code-based question answering in programming education, and reported results from baseline models on tasks like predicting question types and retrieving answers.
We introduce CS1QA, a dataset for code-based question answering in the programming education domain. CS1QA consists of 9,237 question-answer pairs gathered from chat logs in an introductory programming class using Python, and 17,698 unannotated chat data with code. Each question is accompanied with the student's code, and the portion of the code relevant to answering the question. We carefully design the annotation process to construct CS1QA, and analyze the collected dataset in detail. The tasks for CS1QA are to predict the question type, the relevant code snippet given the question and the code and retrieving an answer from the annotated corpus. Results for the experiments on several baseline models are reported and thoroughly analyzed. The tasks for CS1QA challenge models to understand both the code and natural language. This unique dataset can be used as a benchmark for source code comprehension and question answering in the educational setting.