CroLSSim: Cross‐language software similarity detector using hybrid approach of LSA‐based AST‐MDrep features and CNN‐LSTM model

Software similarity in different programming codes is a rapidly evolving field because of its numerous applications in software development, software cloning, software plagiarism, and software forensics. Currently, software researchers and developers search cross-language open-source repositories for similar applications for a variety of reasons, such as reusing programming code, analyzing different implementations, and looking for a better application. However, it is a challenging task because each programming language has a unique syntax and semantic structure. In this paper, a novel tool called Cross-Language Software Similarity (CroLSSim) is designed to detect similar software applications written in different programming codes. First, the Abstract Syntax Tree (AST) features are collected from different programming codes. These are high-quality features that can show the abstract view of each program. Then, Methods Description (MDrep) in combination with AST is used to examine the relationship among different method calls. Second, the Term Frequency Inverse Document Frequency approach is used to retrieve the local and global weights from AST-MDrep features. Third, the Latent Semantic Analysis-based features extraction and selection method is proposed to extract the semantic anchors in reduced dimensional space. Fourth, the Convolution Neural Network (CNN)-based features extraction method is proposed to mine the deep features. Finally, a hybrid deep learning model of CNN-Long-Short-Term Memory is designed to detect semantically similar software applications from these latent variables. The data set contains approximately 9.5K Java, 8.8K C#, and 7.4K C++ software applications obtained from GitHub. The proposed approach outperforms as compared with the state-of-the-art methods

A systematic literature review on source code similarity measurement and clone detection: techniques, applications, and challenges

Measuring and evaluating source code similarity is a fundamental software engineering activity that embraces a broad range of applications, including but not limited to code recommendation, duplicate code, plagiarism, malware, and smell detection. This paper proposes a systematic literature review and meta-analysis on code similarity measurement and evaluation techniques to shed light on the existing approaches and their characteristics in different applications. We initially found over 10000 articles by querying four digital libraries and ended up with 136 primary studies in the field. The studies were classified according to their methodology, programming languages, datasets, tools, and applications. A deep investigation reveals 80 software tools, working with eight different techniques on five application domains. Nearly 49% of the tools work on Java programs and 37% support C and C++, while there is no support for many programming languages. A noteworthy point was the existence of 12 datasets related to source code similarity measurement and duplicate codes, of which only eight datasets were publicly accessible. The lack of reliable datasets, empirical evaluations, hybrid methods, and focuses on multi-paradigm languages are the main challenges in the field. Emerging applications of code similarity measurement concentrate on the development phase in addition to the maintenance.Comment: 49 pages, 10 figures, 6 table

Towards Semantic Clone Detection, Benchmarking, and Evaluation

Developers copy and paste their code to speed up the development process. Sometimes, they copy code from other systems or look up code online to solve a complex problem. Developers reuse copied code with or without modifications. The resulting similar or identical code fragments are called code clones. Sometimes clones are unintentionally written when a developer implements the same or similar functionality. Even when the resulting code fragments are not textually similar but implement the same functionality they are still considered to be clones and are classified as semantic clones. Semantic clones are defined as code fragments that perform the exact same computation and are implemented using different syntax. Software cloning research indicates that code clones exist in all software systems; on average, 5% to 20% of software code is cloned. Due to the potential impact of clones, whether positive or negative, it is essential to locate, track, and manage clones in the source code. Considerable research has been conducted on all types of code clones, including clone detection, analysis, management, and evaluation. Despite the great interest in code clones, there has been considerably less work conducted on semantic clones. As described in this thesis, I advance the state-of-the-art in semantic clone research in several ways. First, I conducted an empirical study to investigate the status of code cloning in and across open-source game systems and the effectiveness of different normalization, filtering, and transformation techniques for detecting semantic clones. Second, I developed an approach to detect clones across .NET programming languages using an intermediate language. Third, I developed a technique using an intermediate language and an ontology to detect semantic clones. Fourth, I mined Stack Overflow answers to build a semantic code clone benchmark that represents real semantic code clones in four programming languages, C, C#, Java, and Python. Fifth, I defined a comprehensive taxonomy that identifies semantic clone types. Finally, I implemented an injection framework that uses the benchmark to compare and evaluate semantic code clone detectors by automatically measuring recall