NLPCode: using natural language processing techniques for automated code refactoring

• Grant number: 20/02679-4
• Support Opportunities: Scholarships in Brazil - Master
• Effective date (Start): June 01, 2021
• Effective date (End): October 31, 2022
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Marcelo Finger
• Grantee: Alan Barzilay
• Host Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Host Company: Universidade de São Paulo (USP). Centro de Inovação da USP (INOVA)
• Associated research grant: 19/07665-4 - Center for Artificial Intelligence, AP.PCPE
• Associated scholarship(s): 21/10104-4 - Function extraction with Transformers, BE.EP.MS

Abstract

Natural Language Processing techniques can be applied to text in general, not only to human language but also for artificial languages such as software code. Code refactoring is a fundamental software engineering technique used both as a quality assurance tool and an important step in code correction and functionality enhancement. In this work, we intend to propose a novel code refactoring model.By utilizing source code as input to our model we wish to obtain automated suggestions of code refactoring in order to achieve better readability and attain good practices in general. The proposed model consists of a neural network that receives a distributed (vectorial) representation of the source code and outputs a representation of the suggested refactored code. This network will be trained based on a repository of refactored code provided through a collaboration with TU Delft Holland. (AU)