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Register a new userCommitBERT: Commit Message Generation Using Pre-Trained Programming Language Model
الالتزام: ارتكاب جيل الرسائل باستخدام نموذج لغة البرمجة المدربة مسبقا
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رسالة الالتزام هي وثيقة تلخص تغييرات شفرة المصدر باللغة الطبيعية. تظهر رسالة الالتزام الجيدة بوضوح تغييرات شفرة المصدر، لذلك يعزز هذا التعاون بين المطورين. لذلك، فإن عملنا هو تطوير نموذج يكتب تلقائيا رسالة الالتزام. تحقيقا لهذه الغاية، نقوم بإصدار مجموعات بيانات 345K تتكون من تعديل التعليمات البرمجية وارتكاب الرسائل في ست لغات برمجة (بيثون، PHP، GO، Java، JavaScript، و Ruby). على غرار نموذج الترجمة الآلية العصبية (NMT)، باستخدام DataSet، نطعم تعديل التعليمات البرمجية إلى إدخال التشفير ورسالة الالتزام إلى إدخال فك الترميز وقياس نتيجة رسالة الالتزام التي تم إنشاؤها مع BLEU-4. أيضا، نقترح طرق التدريب التالية لتحسين نتيجة توليد رسالة الالتزام: (1) طريقة لمعالجة المدخلات لإطعام تعديل التعليمات البرمجية إلى إدخال التشفير. (2) طريقة تستخدم الوزن الأولي مناسب لمجال التعليمات البرمجية لتقليل الفجوة في التمثيل السياقي بين لغة البرمجة (PL) واللغة الطبيعية (NL).
Commit message is a document that summarizes source code changes in natural language. A good commit message clearly shows the source code changes, so this enhances collaboration between developers. Therefore, our work is to develop a model that automatically writes the commit message. To this end, we release 345K datasets consisting of code modification and commit messages in six programming languages (Python, PHP, Go, Java, JavaScript, and Ruby). Similar to the neural machine translation (NMT) model, using our dataset, we feed the code modification to the encoder input and the commit message to the decoder input and measure the result of the generated commit message with BLEU-4. Also, we propose the following two training methods to improve the result of generating the commit message: (1) A method of preprocessing the input to feed the code modification to the encoder input. (2) A method that uses an initial weight suitable for the code domain to reduce the gap in contextual representation between programming language (PL) and natural language (NL).
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