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Register a new userA Simple Approach for Handling Out-of-Vocabulary Identifiers in Deep Learning for Source Code
نهج بسيط للتعامل مع المعرفات خارج المفردات في التعلم العميق للحصول على شفرة المصدر
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هناك مصلحة ناشئة في تطبيق نماذج معالجة اللغة الطبيعية لمهام معالجة التعليمات البرمجية المصدر.أحد المشاكل الرئيسية في تطبيق التعلم العميق لهندسة البرمجيات هو أن الكود المصدري غالبا ما يحتوي على الكثير من المعرفات النادرة، مما يؤدي إلى مفردات ضخمة.نقترح طريقة بسيطة، ولكنها فعالة، بناء على معرفة الهوية المعرفية، للتعامل مع المعرفات خارج المفردات (OOV).يمكن التعامل مع طريقتنا كخطوة مسبقة مسبقا، وبالتالي، تسمح بتنفيذ سهولة.نظرا لأن طريقة إخفاء مصطلحات OOV المقترحة تحسن بشكل كبير من أداء المحول في مهام معالجة التعليمات البرمجية: إكمال التعليمات البرمجية وإصلاح الأخطاء.
There is an emerging interest in the application of natural language processing models to source code processing tasks. One of the major problems in applying deep learning to software engineering is that source code often contains a lot of rare identifiers, resulting in huge vocabularies. We propose a simple, yet effective method, based on identifier anonymization, to handle out-of-vocabulary (OOV) identifiers. Our method can be treated as a preprocessing step and, therefore, allows for easy implementation. We show that the proposed OOV anonymization method significantly improves the performance of the Transformer in two code processing tasks: code completion and bug fixing.
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