Code Comprehension using Classical and Quantum CNN

Abstract

Software developers often spend more time reading and understanding existing source code than writing new code. As a result, improving code comprehensibility can significantly enhance software development and maintenance by reducing the cognitive effort required to grasp program functionality. Code comments play a critical role in this process, helping developers navigate and maintain software more efficiently. However, numerous software projects suffer from missing, outdated, or inconsistent comments, forcing developers to infer functionality directly from the code. The design of programming languages also impacts code comprehension; as previous research suggests; different programming paradigms influence the ease with which developers understand code. This insight has driven researchers to explore machine learning (ML) and deep learning (DL) techniques for a variety of software engineering tasks, including testing, vulnerability detection, and source code analysis. Given the rapid growth in this research area, it has become increasingly difficult for the community to keep track of existing approaches and environments. Recently, ML and DL techniques have been widely adopted for tasks such as source code representation, quality analysis, and automated testing. In this paper, we introduce a novel quantum deep learning method that demonstrates superior performance over classical approaches in the comprehension of source code. Our approach highlights the potential of quantum computing to enhance machine learning techniques applied in the software engineering domain.