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32nd IEEE/ACM International Conference on Program Comprehension (ICPC)

作者： Khajezade, Mohamad Wu, Jie J. W. Fard, Fatemeh Hendijani Rodriguez-Perez, Gema Shehata, Mohamed Sami Univ British Columbia Kelowna BC Canada

ISBN: (纸本)9798400705861

Large Language Models (LLMs) have demonstrated remarkable success in various natural language processing and software engineering tasks, such as code generation. The LLMs are mainly utilized in the prompt-based zero/few-shot paradigm to guide the model in accomplishing the task. GPT-based models are one of the popular ones studied for tasks such as code comment generation or test generation. These tasks are 'generative' tasks. However, there is limited research on the usage of LLMs for 'non-generative' tasks such as classification using the prompt-based paradigm. In this preliminary exploratory study, we investigated the applicability of LLMs for code clone detection (CCD), a non-generative task. By building a mono-lingual and cross-lingual CCD dataset derived from codeNet, we first investigated two different prompts using ChatGPT to detect Type-4 code clones in Java-Java and Java-Ruby pairs in a zero-shot setting. We then conducted an analysis to understand the strengths and weaknesses of ChatGPT in CCD. ChatGPT surpasses the baselines in cross-language CCD attaining an F1-score of 0.877 and achieves comparable performance to fully fine-tuned models for mono-lingual CCD, with an F1-score of 0.878. Also, the prompt and the difficulty level of the problems has an impact on the performance of ChatGPT. Finally, we provide insights and future directions based on our initial analysis(1).

关键词： Large Language Models code clone detection Zero-shot Learning Few-shot Learning.

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C4: Contrastive Cross-Language code clone detection 30

C4: Contrastive Cross-Language Code Clone Detection

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30th IEEE/ACM International Conference on Program Comprehension (ICPC)

作者： Tao, Chenning Zhan, Qi Hu, Xing Xia, Xin Zhejiang Univ Hangzhou Peoples R China Zhejiang Univ Sch Software Technol Ningbo Peoples R China Software Engn Applicat Technol Lab Huawei Peoples R China

ISBN: (纸本)9781450392983

During software development, developers introduce code clones by reusing existing code to improve programming productivity. Considering the detrimental effects on software maintenance and evolution, many techniques are proposed to detect code clones. Existing approaches are mainly used to detect clones written in the same programming language. However, it is common to develop programs with the same functionality but in different programming languages to support various platforms. In this paper, we propose a new approach named C4, referring to Contrastive Cross-language code clone detection model. It can detect cross-language clones with learned representations effectively. C4 exploits the pre-trained model codeBERT to convert programs in different languages into high-dimensional vector representations. In addition, we fine tune the C4 model through a constrastive learning objective that can effectively recognize clone pairs and non-clone pairs. To evaluate the effectiveness of our approach, we conduct extensive experiments on the dataset proposed by CLCDSA. Experimental results show that C4 achieves scores of 0.94, 0.90, and 0.92 in terms of precision, recall and F-measure and substantially outperforms the state-of-the-art baselines.

关键词： code clone detection Neural Networks Cross-Language Contrastive Learning

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Learning-Based Recursive Aggregation of Abstract Syntax Trees for code clone detection 26

Learning-Based Recursive Aggregation of Abstract Syntax Tree...

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26th IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Buech, Lutz Andrzejak, Artur Heidelberg Univ Inst Comp Sci Heidelberg Germany

ISBN: (纸本)9781728105918

code clone detection remains a crucial challenge in maintaining software projects. Many classic approaches rely on handcrafted aggregation schemes, while recent work uses supervised or unsupervised learning. In this work, we study several aspects of aggregation schemes for code clone detection based on supervised learning. To this aim, we implement an AST-based Recursive Neural Network. Firstly, our ablation study shows the influence of model choices and hyperparameters. We introduce error scaling as a way to effectively and efficiently address the class imbalance problem arising in code clone detection. Secondly, we study the influence of pretrained embeddings representing nodes in ASTs. We show that simply averaging all node vectors of a given AST yields strong baseline aggregation scheme. Further, learned AST aggregation schemes greatly benefit from pretrained node embeddings. Finally, we show the importance of carefully separating training and test data by clone clusters, to reliably measure generalization of models learned with supervision.

关键词： code clone detection Abstract Syntax Trees Embeddings Recursive Neural Network Siamese Network

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Adaptive Prefix Filtering for Accurate code clone detection in Conjunction with Meta-learning

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SN Computer Science 2024年第6期5卷 789页

作者： Ralhan, Chavi Malik, Navneet Agrawal, Prateek Gupta, Charu jatana, Nishtha Jatain, Divya Sharma, Geetanjali Lovely Professional University Punjab Phagwara India Shree Guru Gobind Singh Tricentenary University Haryana Gurugram India Bhagwan Parshuram Institute of Technology New Delhi India Maharaja Surajmal Institute of Technology New Delhi India

This research project has significantly contributed to the advancement of code duplication detection by introducing a new testing method and constructing a highly accurate meta-classifier. The evaluation of various architectures and utilization of a diverse dataset enabled the development of a novel and versatile solution for detecting duplicate code in both Java and Python programming languages. The proposed algorithm relies on a set of distinctive qualities represented by 17 code metrics. It efficiently stores the code structure in Abstract Prefix filtering (APF) to identify duplicate code segments. A comprehensive series of experiments is conducted to evaluate the effectiveness of different architectures for meta-classifiers. The goal was to identify an optimal 2-layer stacking meta-classifier to yield precise and reliable results. This process successfully constructed a novel classifier, demonstrating exceptional accuracy in detecting duplicate code. The algorithm was trained on a dataset comprising 19,988 data points, encompassing code metrics from both Java and Python programming languages. This diverse dataset enabled the model to learn and generalize across multiple language paradigms, enhancing its versatility and effectiveness in code clone detection. The results show that the proposed model outperformed the state-of-the-art models, which proves that it is the appropriate choice for constructing a meta-classifier for cloned code detection. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Abstract prefix filtering code clone detection Java Meta-learning Programming languages Python

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Binary code clone detection across Architectures and Compiling Configurations 25

Binary Code Clone Detection across Architectures and Compili...

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25th IEEE/ACM International Conference on Program Comprehension (ICPC)

作者： Hu, Yikun Zhang, Yuanyuan Li, Juanru Gu, Dawu Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9781538605356

Binary code clone detection (or similarity comparison) is a fundamental technique for many important applications, such as plagiarism detection, malware analysis, software vulnerability assessment and program comprehension. With the prevailing of smart and IoT (Internet of Things) devices, more and more programs are ported from traditional desktop platforms (e.g., IA-32) to ARM and MIPS architectures. It becomes imperative to detect cloned binary code across architectures. However, because of incomparable instruction sets of different architectures as well as alternative compiling configurations, it is difficult to conduct a binary code clone detection with traditional syntax-or structure-based methods. To address, we propose a semantics-based approach to fulfill the target. We recognize arguments and indirect jump targets of each binary function, and emulate executions of those functions, extracting semantic signatures to measure the similarity of functions. The approach has been implemented in a prototype system named CACOMPARE to detect cloned binary functions across architectures and compiling configurations. It supports comparisons between mainstream architectures (IA-32, ARM and MIPS) and is able to analyse binaries on the Linux platform. The experimental results show that CACOMPARE not only is effective in dealing with binaries of different architectures and variant compiling configurations, but also improves the accuracy of binary code clone detection comparing to state-of-the-art solutions.

关键词： binary program analysis code clone detection reverse engineering static analysis

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Functional code clone detection with Syntax and Semantics Fusion Learning 2020

Functional Code Clone Detection with Syntax and Semantics Fu...

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29th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA)

作者： Fang, Chunrong Liu, Zixi Shi, Yangyang Huang, Jeff Shi, Qingkai Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Texas A&M Univ Parasol Lab College Stn TX 77843 USA Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9781450380089

clone detection of source code is among the most fundamental software engineering techniques. Despite intensive research in the past decade, existing techniques are still unsatisfactory in detecting "functional" code clones. In particular, existing techniques cannot efficiently extract syntax and semantics information from source code. In this paper, we propose a novel joint code representation that applies fusion embedding techniques to learn hidden syntactic and semantic features of source codes. Besides, we introduce a new granularity for functional code clone detection. Our approach regards the connected methods with caller-callee relationships as a functionality and the method without any caller-callee relationship with other methods represents a single functionality. Then we train a supervised deep learning model to detect functional code clones. We conduct evaluations on a large dataset of C++ programs and the experimental results show that fusion learning can significantly outperform the state-of-the-art techniques in detecting functional code clones.

关键词： code clone detection functional clone detection code representation syntax and semantics fusion learning

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TCNAS: TRANSFORMER ARCHITECTURE EVOLVING IN code clone detection 49

TCNAS: TRANSFORMER ARCHITECTURE EVOLVING IN CODE CLONE DETEC...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xu, Hongyan Pei, Xiaohuan Su, Xiu You, Shan Xu, Chang Cent South Univ Big Data Inst Changsha Peoples R China Univ New South Wales Sch Comp Sci & Engn Kensington Australia Commonwealth Sci & Ind Res Org CSIRO Data61 Canberra Australia Univ Sydney Sch Comp Sci Fac Engn Sydney Australia SenseTime Res Hong Kong Peoples R China

ISBN: (纸本)9798350344868;9798350344851

code clone detection aims at finding code fragments with syntactic or semantic similarity. Most of current approaches mainly focus on detecting syntactic similarity while ignoring semantic long-term context alignment, and these detection methods encode the source code using human-designed models, a process which requires both expert input and a significant cost of time for experimentation and refinement. To address these challenges, we introduce the Transformer code Neural Architecture Search (TCNAS), an approach designed to optimize transformer-based architectures for detection. In TCNAS, all channels are trained and evaluated equitably to enhance search efficiency. Besides, we introduce the dataflow of the code by extracting the semantic information from the code fragments. TCNAS facilitates the discovery of an optimal model structure geared towards the detection, eliminating the need for manual design. The searched optimal architecture is utilized to detect the code pairs. We conduct various empirical experiments on the benchmark, which covering all four types of code clone detection. The results demonstrate our approach consistently yields competitive detection scores across a range of evaluations.

关键词： code clone detection deep learning Transformer neural architecture search

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Precise code clone detection with Architecture of Abstract Syntax Trees 17th

Precise Code Clone Detection with Architecture of Abstract S...

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17th International Conference on Wireless Algorithms, Systems, and Applications (WASA)

作者： Guo, Xin Zhang, Ruyun Zhou, Lu Lu, Xiaozhen Nanjing Univ Aeronaut & Astronaut Nanjing Peoples R China Zhejiang Lab Hangzhou Peoples R China

ISBN: (纸本)9783031192104;9783031192111

In the field of code clone detection, there are token-based similarity and abstract syntax tree-based detection methods. The former consumes less resources and is faster to detect, while the latter consumes more space and is less efficient. And there are few tools that scale to large-scale databases. To address the challenges, an approach is proposed that can detect code clones using the similarity of tokens and architecture of abstract syntax trees. Architecture of the syntax trees preserves the precision of detecting clone pairs, at the same time, the method also preserves the speed of matching code similarity. In the approach, it first parses the tokens of the code fragments and gets the features of the syntax trees. It can eliminate the unqualified parts of them based on the architecture when matching the candidates quickly, and then detects the similarity in detail. Finally, the results are output according to the input threshold range. The experiments confirm that the method substantially improves the precision of code clone detection while keeping the recall rate unabated.

关键词： code clone detection Abstract syntax tree Large-scale database

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CMCD: Count Matrix based code clone detection

CMCD: Count Matrix based Code Clone Detection

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18th Asia-Pacific Software Engineering Conference (APSEC)

作者： Yuan, Yang Guo, Yao Peking Univ Natl Engn Res Ctr Software Engn Key Lab High Confidence Software Technol Minist EducSch Elect Engn & Comp Sci Beijing 100871 Peoples R China

ISBN: (纸本)9780769546094

This paper introduces CMCD, a Count Matrix based technique to detect clones in program code. The key concept behind CMCD is Count Matrix, which is created while counting the occurrence frequencies of every variable in situations specified by pre-determined counting conditions. Because the characteristics of the count matrix do not change due to variable name replacements or even switching of statements, CMCD works well on many hard-to-detect code clones, such as swapping statements or deleting a few lines, which are difficult for other state-of-the-art detection techniques. We have obtained the following interesting results using CMCD: (1) we successfully detected all 16 clone scenarios proposed by C. Roy et al.;(2) we discovered two clone clusters with three copies each from 29 student-submitted compiler lab projects;(3) we identified 174 code clone clusters and a potential bug from JDK 1.6 source files.

关键词： code clone detection count matrix bipartite graph matching

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Generalizability of code clone detection on codeBERT 7

Generalizability of Code Clone Detection on CodeBERT

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37th IEEE/ACM International Conference on Automated Software Engineering (ASE)

作者： Sonnekalb, Tim Gruner, Bernd Brust, Clemens-Alexander Maeder, Patrick German Aeorospace Ctr DLR Inst Data Sciene Jena Germany Tech Univ Ilmenau Data Intens Syst & Visualizat Grp Ilmenau Germany

ISBN: (纸本)9781450394758

Transformer networks such as codeBERT already achieve outstanding results for code clone detection in benchmark datasets, so one could assume that this task has already been solved. However, code clone detection is not a trivial task. Semantic code clones, in particular, are challenging to detect. We show that the generalizability of codeBERT decreases by evaluating two different subsets of Java code clones from Big-cloneBench. We observe a significant drop in F1 score when we evaluate different code snippets and functionality IDs than those used for model building.

关键词： transformer code clone detection bigclonebench codebert codexglue

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