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

GPTCloneBench: A comprehensive benchmark of semantic clones and cross-language clones using GPT-3 model and SemanticCloneBench

With the emergence of Machine Learning, there has been a surge in leveraging its capabilities for problem-solving across various domains. In the code clone realm, the identification of type-4 or semantic clones has emerged as a crucial yet challenging task. Researchers aim to utilize Machine Learning to tackle this challenge, often relying on the BigCloneBench dataset. However, it's worth noting that BigCloneBench, originally not designed for semantic clone detection, presents several limitations that hinder its suitability as a comprehensive training dataset for this specific purpose. Furthermore, CLCDSA dataset suffers from a lack of reusable examples aligning with real-world software systems, rendering it inadequate for cross-language clone detection approaches. In this work, we present a comprehensive semantic clone and cross-language clone benchmark, GPTCloneBench by exploiting SemanticCloneBench and OpenAI's GPT-3 model. In particular, using code fragments from SemanticCloneBench as sample inputs along with appropriate prompt engineering for GPT-3 model, we generate semantic and cross-language clones for these specific fragments and then conduct a combination of extensive manual analysis, tool-assisted filtering, functionality testing and automated validation in building the benchmark. From 79,928 clone pairs of GPT-3 output, we created a benchmark with 37,149 true semantic clone pairs, 19,288 false semantic pairs(Type-1/Type-2), and 20,770 cross-language clones across four languages (Java, C, C#, and Python). Our benchmark is 15-fold larger than SemanticCloneBench, has more functional code examples for software systems and programming language support than CLCDSA, and overcomes BigCloneBench's qualities, quantification, and language variety limitations.Comment: Accepted in 39th IEEE International Conference on Software Maintenance and Evolution(ICSME 2023

Detecting Clones Across Microsoft .NET Programming Languages

Abstract—The Microsoft.NET framework and its language family focus on multi-language development to support interoperability across several programming languages. The framework allows for the development of similar applications in different languages through the reuse of core libraries. As a result of such a multi-language development, the identification and traceability of similar code fragments (clones) becomes a key challenge. In this paper, we present a clone detection approach for the.NET language family. The approach is based on the Common Intermediate Language, which is generated by the.NET compiler for the different languages within the.NET framework. In order to achieve an acceptable recall while maintaining the precision of our detection approach, we define a set of filtering processes to reduce noise in the raw data. We show that these filters are essential for Intermediate Language-based clone detection, without significantly affecting the precision of the detection approach. Finally, we study the quantitative and qualitative performance aspects of our clone detection approach. We evaluate the number of reported candidate clone-pairs, as well as the precision and recall (using manual validation) for several open source cross-language systems, to show the effectiveness of our proposed approach. Index Terms—cross-language clone detection, intermediate language, binary, multi-language, similarity component. I

GPTCloneBench: A comprehensive benchmark of semantic clones and cross-language clones using GPT-3 model and SemanticCloneBench

<p>This is the full dataset of GPTCloneBench (version 2)</p&gt