Clone detection plays an important role in software engineering. Finding clones within a single project introduces possible refactoring opportunities, and between different projects it could be used for detecting code reuse or possible licensing violations.
In this paper, we propose a modification to bag-of-tokens based clone detection that allows detecting more clone pairs of greater diversity without losing precision by implementing a multi-threshold search, i.e. conducting the search several times, aimed at different groups of clones. To combat the increase in operation time that this approach brings about, we propose an optimization that allows to significantly decrease the overlap in detected clones between the searches.
We evaluate the method for two different popular clone detection tools on two datasets of different sizes. The implementation of the technique allows to increase the number of detected clones by 40.5-56.6% for different datasets. BigCloneBench evaluation also shows that the recall of detecting Strongly Type-3 clones increases from 37.5% to 59.6%.
Programmers often reuse code from source code repositories to reduce the development effort. Code clones are candidates for reuse in exploratory or rapid development, as they represent often repeated functionality in software systems. To facilitate code clone reuse, we propose DeepClone, a novel approach utilizing a deep learning algorithm for modeling code clones to predict the next set of tokens (possibly a complete clone method body) based on the code written so far. The predicted tokens require minimal customization to fit the context. DeepClone applies natural language processing techniqu…
Finding the same or similar code snippets in source code is one of fundamental activities in software maintenance. Text-based pattern matching tools such as grep is frequently used for such purpose, but making proper queries for the expected result is not easy. Code clone detectors could be used but their features and result are generally excessive. In this paper, we propose Code Clone matching (CC matching for short) that employs a combination of token-based clone detection and meta-patterns enhanced with meta-tokens. The user simply gives a query code snippet possibly with a few meta-tokens …
In this paper, we present an approach for transferring an optimal lower size threshold for clone detection from one language to another by analyzing their clone distributions. We showcase this method by transferring the threshold from regular Python scripts to Jupyter notebooks for using in two JetBrains IDEs, Datalore and DataSpell.
Type-4 clones refer to a pair of code snippets with similar semantics but written in different syntax, which challenges the existing code clone detection techniques. Previous studies, however, highly rely on syntactic structures and textual tokens, which cannot precisely represent the semantic information of code and might introduce non-negligible noise into the detection models. To overcome these limitations, we design a novel semantic graph-based deep detection approach, called SEED. For a pair of code snippets, SEED constructs a semantic graph of each code snippet based on intermediate repr…
Code clone is a serious problem in software and has the potential to software defects, maintenance overhead, and licensing violations. Therefore, clone detection is important for reducing maintenance effort and improving code quality during software evolution. A variety of clone detection techniques have been proposed to identify similar code in software. However, few of them can efficiently detect semantic clones (functionally similar code without any syntactic resemblance). Recently, several deep learning based clone detectors are proposed to detect semantic clones. However, these approaches…
Code clones are duplicate code fragments that share (nearly) similar syntax or semantics. Code clone detection plays an important role in software maintenance, code refactoring, and reuse. A substantial amount of research has been conducted in the past to detect clones. A majority of these approaches use lexical and syntactic information to detect clones. However, only a few of them target semantic clones. Recently, motivated by the success of deep learning models in other fields, including natural language processing and computer vision, researchers have attempted to adopt deep learning techn…
Recent successes in training word embeddings for NLP tasks have encouraged a wave of research on representation learning for source code, which builds on similar NLP methods. The overall objective is then to produce code embeddings that capture the maximum of program semantics. State-of-the-art approaches invariably rely on a syntactic representation (i.e., raw lexical tokens, abstract syntax trees, or intermediate representation tokens) to generate embeddings, which are criticized in the literature as non-robust or non-generalizable. In this work, we investigate a novel embedding approach bas…
HGAdapter: Hypergraph-based Adapters in Language Models for Code Summarization and Clone Detection
Data Technology Group, Technology Research and Development Department, Guotai Haitong Securities, China（国泰君安证券数据技术部、技术研发部）；School of Computer Science and Technology, East China Normal University, China（华东师范大学计算机科学与技术学院）
Pluvio: Assembly Clone Search for Out-of-domain Architectures and Libraries through Transfer Learning and Conditional Variational Information Bottleneck
McGill University（麦吉尔大学）；Defence Research and Development Canada (DRDC)（加拿大国防研究与发展部）
Seeing is Coding: On the Effectiveness of Vision Language Models in Code Understanding
Shanghai Jiao Tong University China；Hohai University China；Singapore Management University Singapore；Imperial College London United Kingdom；East China Normal University \& Shanghai Innovation Institute China；Chongqing University China；Shanghai Jiao Tong University；Hohai University；Singapore Management University；Imperial College London；East China Normal University \& Shanghai Innovation Institute；Chongqing University
