CodeGuard: enhancing accuracy in detecting clones within java source code by Yasir Glani, Luo Ping

Subjects: “…code clone detection…”
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CODE CLONE DETECTION WITH SELF-SUPERVISION ON DUAL GRAPHS by Chunguang Li, Adisorn Sirikham, Jessada Konpang, Yan Wang

Subjects: “…Code Clone Detection…”
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Multilingual Investigation of Cross-Project Code Clones in Open-Source Software for Internet of Things Systems by Wenqing Zhu, Norihiro Yoshida, Yutaka Matsubara, Hiroaki Takada

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Syntactic–Semantic Detection of Clone-Caused Vulnerabilities in the IoT Devices by Maxim Kalinin, Nikita Gribkov

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SPRD:fast application repackaging detection approach in Android based on application’s UI and program dependency graph by Run WANG, Li’na WANG, Benxiao TANG, Lei ZHAO

“…A two stage detection approach which combine application’s UI and program code based on the observation that repackaging applications merely modify the structure of their user interface was proposed.Firstly,a fast hash similarity detection technique based on an abstracted representation of UI to identify the potential visual-similar repackaging applications was designed.Secondly,program dependency graph is used to represent as the feature of app to achieve fine-grained and precise code clone detection.A prototype system,SPRD,was implemented based on the proposed approach.Experimental results show that the proposed approach achieves a good performance in both scalability and accuracy,and can be effectively applied in millions of applications and billions of code detection.…”
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LeONet: A Hybrid Deep Learning Approach for High-Precision Code Clone Detection Using Abstract Syntax Tree Features by Thanoshan Vijayanandan, Kuhaneswaran Banujan, Ashan Induranga, Banage T. G. S. Kumara, Kaveenga Koswattage

“…It also compared favorably against state-of-the-art approaches, indicating its effectiveness in code clone detection. The results validate the effectiveness of LeONet in detecting code clones, outperforming existing classifiers and competing closely with advanced methods. …”
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Tree Slicing in Clone Detection: Syntactic Analysis Made (Semi)-Semantic by Marat Akhin, Vladimir Itsykson

“…A lot of different clone detection approaches have been proposed over the years to deal with this problem, but almost all of them do not consider semantic properties of the source code. …”
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Machine Learning Approaches to Code Similarity Measurement: A Systematic Review by Zixian Zhang, Takfarinas Saber

“…These include but are not limited to code quality assurance, code review processes, code plagiarism detection, security, and vulnerability analysis. …”
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Using pseudo-AI submissions for detecting AI-generated code by Shariq Bashir

“…On the other side, the presence of these pseudo-AI submissions reinforces the expectation for students to produce unique and personalized work, motivating them to engage more deeply with the material and rely on their own problem-solving skills.ResultsA user study indicates that this method can detect AI-generated code with over 96% accuracy.DiscussionThe analysis of results shows that pseudo-AI submissions created using AI tools do not closely resemble student-written code, suggesting that the framework does not hinder students from writing their own unique solutions. …”
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Unsupervised Machine Learning for Effective Code Smell Detection: A Novel Method by Ruchin Gupta, Narendra Kumar, Sunil Kumar, Jitendra Kumar Seth

“…This method is especially beneficial in situations where labeled data is scarce or unavailable and can be used to identify new code smells, generate labeled data for SML and detect multiple code smells simultaneously within a codebase.…”
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MSSA: multi-stage semantic-aware neural network for binary code similarity detection by Bangrui Wan, Jianjun Zhou, Ying Wang, Feng Chen, Ying Qian

“…Binary code similarity detection (BCSD) aims to identify whether a pair of binary code snippets is similar, which is widely used for tasks such as malware analysis, patch analysis, and clone detection. …”
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Detection of structural variants linked to mutton flavor and odor in two closely related black goat breeds by Lingle Chang, Xi Niu, Shihui Huang, Derong Song, Xueqin Ran, Jiafu Wang

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A Deep Learning Approach for a Source Code Detection Model Using Self-Attention by Yao Meng, Long Liu

“…With the development of deep learning, many approaches based on neural networks are proposed for code clone. In this paper, we propose a novel source code detection model At-biLSTM based on a bidirectional LSTM network with a self-attention layer. …”
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A Novel Approach for Enhancing Code Smell Detection Using Random Convolutional Kernel Transform by Mostefai Abdelkader, Mekour Mansour

“… Context: In software engineering, the presence of code smells is closely associated with increased maintenance costs and complexities, making their detection and remediation an important concern. …”
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Multi-pulse Fourier codes for bit transmission at the quantum limit by Matteo Rosati

“…We show that multi-pulse codes can approach the rate of OOK closely, providing a simplified design for quantum-enhanced communication in the photon-starved regime; furthermore, multi-level codes can approach generalized-OOK strategies with multiple pulse types, thus they can be employed in the larger photon-number regime.…”
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Optimal Design of Linear Space Code for MIMO Optical Wireless Communications by Yan-Yu Zhang, Hong-Yi Yu, Jian-Kang Zhang, Yi-Jun Zhu

“…In this paper, the design of linear full-diversity space code (FDSC) is considered for an intensity-modulated direct-detection multiple-input–multiple-output optical wireless communication (IM/DD MIMO-OWC) system, in which the channel suffers from log-normal fading and different links have different variances. …”
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Code-mixing unveiled: Enhancing the hate speech detection in Arabic dialect tweets using machine learning models. by Ali Alhazmi, Rohana Mahmud, Norisma Idris, Mohamed Elhag Mohamed Abo, Christopher Ifeanyi Eke

“…Recognizing this research vacuum, the study aims to close it by examining how well machine learning models containing variation features can detect hate speech, especially when it comes to Arabic tweets featuring code-mixing. …”
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Long non-coding RNA Malat1 modulates CXCR4 expression to regulate the interaction between induced neural stem cells and microglia following closed head injury by Qin Dong, Pengyu Chen, Wenqiao Qiu, Zhijun Yang, Yanteng Li, Yuhui Zhou, Lili Guo, Dan Zou, Ruxiang Xu, Mou Gao

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AppAuth: Authorship Attribution for Android App Clones by Guoai Xu, Chengpeng Zhang, Bowen Sun, Xinyu Yang, Yanhui Guo, Chengze Li, Haoyu Wang

“…Android app clone detection has been extensively studied in our community, and a number of effective approaches and frameworks were proposed and released. …”
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Hardcoded vulnerability detection approach for IoT device firmware by Chao MU, Xin WANG, Ming YANG, Heng ZHANG, Zhenya CHEN, Xiaoming WU

“…With the popularization of IoT devices, more and more valuable data is generated.Analyzing and mining big data based on IoT devices has become a hot topic in the academic and industrial circles in recent years.However, due to the lack of necessary detection and protection methods, many IoT devices have serious information security risks.In particular, device hard-coded information is closely related to system encryption and decryption, identity authentication and other functions, which can provide confidentiality protection for core data.Once this information is exploited by malicious attackers, serious consequences such as sensitive information leakage, backdoor attacks, and unauthorized logins will occur.In response to this problem, a multi-type character recognition and positioning scheme was designed and a hard-coded vulnerability detection method in executable files was proposed based on the study of the characteristics of hard-coded vulnerabilities in IoT devices.The proposed method extracted the firmware of IoT devices and filtered all executable files as the source to be analyzed.Then, a solution to identify and locate three types of hard-coded characters was provided.Further, the reachability of the function, where the hard-coded character was located, was analyzed according to the function call relationship.Meanwhile, the instruction heterogeneity was mitigated by an intermediate representation (IR) model.The character and parameter hard-coded values was obtained through a data flow analysis approach.A symbolic execution method was devised to determine the trigger conditions of the hard-coded vulnerabilities, and then the vulnerability detection result was output.On the one hand, the proposed method introduced the method of symbolic execution based on the use of the intermediate representation model, which eliminated the dependency of instruction architecture and reduces the false positive rate of vulnerabilities; On the other hand, this method can integrate characters, files, and cryptographic implementation to realize the different characteristics of three types of hard-coded characters, which increased the coverage of vulnerability detection and improves the versatility of the detection method.The experimental results show that the proposed method can effectively detect three types of hard-coded vulnerabilities of characters, files and cryptographic implementation in various IoT devices, and has good detection accuracy, which can provide certain guidance for the deployment of subsequent security protection technologies.…”
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