Deep learning similarities from different representations of source code

• Published in: 2018 IEEE/ACM 15th International Conference on Mining Software Repositories (MSR) pp. 542 - 553
• Main Authors: Tufano, Michele, Watson, Cody, Bavota, Gabriele, Di Penta, Massimiliano, White, Martin, Poshyvanyk, Denys
• Format: Conference Proceeding
• Language: English
• Published: New York, NY, USA ACM 28.05.2018
• Series: ACM Conferences
• Subjects: Cloning; code similarities; Computational modeling; Computing methodologies > Machine learning > Machine learning approaches > Neural networks; Deep learning; Feature extraction; neural networks; Software; Software and its engineering > Software creation and management > Software development techniques > Reusability; Syntactics; Task analysis; deep learning; code similarities; neural networks
• ISBN: 9781450357166; 1450357164
• ISSN: 2574-3864
• DOI: 10.1145/3196398.3196431

Assessing the similarity between code components plays a pivotal role in a number of Software Engineering (SE) tasks, such as clone detection, impact analysis, refactoring, etc. Code similarity is generally measured by relying on manually defined or hand-crafted features, e.g., by analyzing the overlap among identifiers or comparing the Abstract Syntax Trees of two code components. These features represent a best guess at what SE researchers can utilize to exploit and reliably assess code similarity for a given task. Recent work has shown, when using a stream of identifiers to represent the code, that Deep Learning (DL) can effectively replace manual feature engineering for the task of clone detection. However, source code can be represented at different levels of abstraction: identifiers, Abstract Syntax Trees, Control Flow Graphs, and Bytecode. We conjecture that each code representation can provide a different, yet orthogonal view of the same code fragment, thus, enabling a more reliable detection of similarities in code. In this paper, we demonstrate how SE tasks can benefit from a DL-based approach, which can automatically learn code similarities from different representations.