The code clone detection issue has been researched using a number of explicit factors based on the tokens and contents and found effective results. However, exposing code contents may be an impractical option because of privacy and security factors. Moreover, the lack of scalability of past methods is an important challenge. The code flow states can be inferred by code structure and implicitly represented using empirical graphs. The assumption is that modelling of the code clone detection problem can be achieved without the content of the codes being revealed. Here, a Graph-of-Code concept for the code clone detection problem is introduced, which represents codes into graphs. While Graph-of-Code provides structural properties and quantification of its characteristics, it can exclude code contents or tokens to identify the clone type. The aim is to evaluate the impact of graph-of-code structural properties on the performance of code clone detection. This work employs a feature extraction-based approach for unlabelled graphs. The approach generates a ‘Graph Fingerprint’ which represents different topological feature levels. The results of code clone detection indicate that code structure has a significant role in detecting clone types. We found different GoC-models outperform others. The models achieve between 96% to 99% in detecting code clones based on recall, precision, and F1-Score. The GoC approach is capable in detecting code clones with scalable dataset and with preserving codes privacy.

Code clones introduce difficulties in software maintenance and cause bug propagation. We propose a framework for detecting Java code obfuscation and both syntactic and semantic clones by adding cluster data which is using the sequential information bottleneck algorithm with (CNN) deep learing classification, called CCDLC. The CCDLC uses a novel Java bytecode dependency graph (BDG) along with program dependency graph (PDG) and abstract syntax tree (AST) features. We use several publicly available code clone and Java obfuscated code datasets for validating effectiveness of our framework. Our experimental results and evaluation indicate that using the combination of clustering and deep learning classification is a viable methodology, since they improve detecting clones and obfuscation code on the corpus. The key benefit of this approach is that our tool can improve detecting obfuscation accuracy about 5.44% and improve finding both Syntactic and Semantic clones accuracy about 12%.

Machine learning approaches built to identify code clones fail to perform well due to insufficient training samples and have been restricted only up to Type-III clones. A majority of the publicly available code clone corpora are incomplete in nature and lack labeled samples for semantic or Type-IV clones. We present here two schemes for labeling all types of clones including Type-IV clones. We restrict our study to Java code only. First, we use an unsupervised approach to label Type-IV clones and validate them using expert Java programmers. Next, we present a supervised scheme for labeling (or classifying) unknown samples based on labeled samples derived from our first scheme. We evaluate the performance of our schemes using six well-known Java code clone corpora and report on the quality of produced clones in terms of kappa agreement, mean error and accuracy scores. Results show that both schemes produce high quality code clones facilitating future use of machine learning in detecting clones of Type-IV.

If two fragments of source code are identical to each other, they are called code clones. Code clones introduce difficulties in software maintenance and cause bug propagation. In this paper, we present a machine learning framework to automatically detect clones in software, which is able to detect Types-3 and the most complicated kind of clones, Type-4 clones. Previously used traditional features are often weak in detecting the semantic clones The novel aspects of our approach are the extraction of features from abstract syntax trees (AST) and program dependency graphs (PDG), representation of a pair of code fragments as a vector and the use of classification algorithms. The key benefit of this approach is that our approach can find both syntactic and semantic clones extremely well. Our evaluation indicates that using our new AST and PDG features is a viable methodology, since they improve detecting clones on the IJaDataset 2.0.