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Register a new userMultiFix: Learning to Repair Multiple Errors by Optimal Alignment Learning
Multifix: تعلم إصلاح أخطاء متعددة من خلال تعلم المحاذاة الأمثل
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نحن نعتبر مشكلة تعلم إصلاح برامج ج خاطئة عن طريق تعلم المحاذاة المثلى مع البرامج الصحيحة. نظرا لأن الأساليب السابقة إصلاح خطأ واحد في السطر، فمن المحتمل أنه لا مفر منه لتكرار عملية التثبيت حتى لا تبقى أخطاء. في هذا العمل، نقترح إطارا تعليمي تسلسل تسلسل جديد لتحديد أخطاء برنامج متعددة في وقت واحد. نقدم نهج وضع العلامات على البيانات المسافة من المسافة لتصحيح خطأ البرنامج. بدلا من وصف مثال إصلاح البرنامج عن طريق إقران برنامج خاطئ مع إصلاح خط، نعلم المثال عن طريق تحديد برنامج خاطئ مع محاذاة مثالية إلى البرنامج الصحيح المقابل الناتج عن حساب التحرير المسافة. نقيم نهجنا المقترح في مجموعة بيانات متاحة للجمهور (Deepfix DataSet) التي تتكون من برامج C الخاطئة المقدمة من طلاب البرمجة المبتدئين. على مجموعة من 6،975 برنامجا خاطئا من بيانات DataSet Deepfix، فإن نهجنا يحقق النتيجة الحديثة من حيث معدل الإصلاح الكامل على DeepFix DataSet (بدون بيانات إضافية مثل رسالة خطأ التحويل البرمجي أو رموز مصدر إضافية مسبقا -تمرين).
We consider the problem of learning to repair erroneous C programs by learning optimal alignments with correct programs. Since the previous approaches fix a single error in a line, it is inevitable to iterate the fixing process until no errors remain. In this work, we propose a novel sequence-to-sequence learning framework for fixing multiple program errors at a time. We introduce the edit-distance-based data labeling approach for program error correction. Instead of labeling a program repair example by pairing an erroneous program with a line fix, we label the example by paring an erroneous program with an optimal alignment to the corresponding correct program produced by the edit-distance computation. We evaluate our proposed approach on a publicly available dataset (DeepFix dataset) that consists of erroneous C programs submitted by novice programming students. On a set of 6,975 erroneous C programs from the DeepFix dataset, our approach achieves the state-of-the-art result in terms of full repair rate on the DeepFix dataset (without extra data such as compiler error message or additional source codes for pre-training).
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