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Register a new userCodeQA: A Question Answering Dataset for Source Code Comprehension
Codeqa: سؤال حول DataSet لفهم شفرة المصدر
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نقترح codeqa، وهو سؤالا حرة يرد على مجموعة البيانات لغرض فهم التعليمات البرمجية المصدر: إعطاء مقتطف رمز وسؤال، مطلوب إجابة نصية أن يتم إنشاؤها.يحتوي Codeqa على مجموعة بيانات Java مع 119،778 أزواج من الإجابات السؤال ومجموعة بيانات بيثون مع 70،085 زوجا للإجابة على الإجابات.للحصول على أسئلة وأجوبة طبيعية وفية، نقوم بتنفيذ القواعد النحوية والتحليل الدلالي لتحويل تعليقات التعليمات البرمجية إلى أزواج الإجابة على الأسئلة.نقدم عملية البناء وإجراء التحليل المنهجي لمجموعة البيانات الخاصة بنا.يتم عرض نتائج التجربة التي حققتها العديد من خطوط الأساس العصبية على DataSet لدينا ومناقشتها.في حين أن البحث عن الإجابة على السؤال والفهم قراءة الآلة يتطور بسرعة، إلا أن القليل من العمل السابق قد لفت الانتباه إلى إجابة سؤال التعليمات البرمجية.يمكن أن تكون هذه البيانات الجديدة هذه بمثابة معيار بحث مفيد لفهم شفرة المصدر.
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.
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