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An Automated Approach to Discovering Software Refactorings by Comparing Successive Versions

IEEE Transactions on Software Engineering 2025年第5期51卷 1358-1380页

作者： Liu, Bo Liu, Hui Niu, Nan Zhang, Yuxia Li, Guangjie Jiang, He Jiang, Yanjie University of Cincinnati Department of Electrical Engineering and Computer Science CincinnatiOH United States National Innovation Institute of Defense Technology Beijing China School of Software Dalian University of Technology Dalian China Beijing Institute of Technology School of Computer Science and Technology Beijing China Peking University School of Computer Science Beijing China

Software developers and maintainers frequently conduct software refactorings to improve software quality. Identifying the conducted software refactorings may significantly facilitate the comprehension of software evolution, and thus facilitate software maintenance and evolution. Besides that, the identified refactorings are also valuable for data-driven approaches in software refactoring. To this end, researchers have proposed a few approaches to identifying software refactorings automatically. However, the performance (especially precision) of such approaches deserves substantial improvement. To this end, in this paper, we propose a novel refactoring detection approach, called REEXTRACTOR+. At the heart of REEXTRACTOR+ is a reference-based entity matching algorithm that matches coarse-grained code entities (e.g., classes and methods) between two successive versions, and a context-aware statement matching algorithm that matches statements within a pair of matched methods. We evaluated REEXTRACTOR+ on a benchmark consisting of 400 commits from 20 real-world projects. The evaluation results suggested that REEXTRACTOR+ significantly outperformed the state of the art in refactoring detection, reducing the number of false positives by 59.6% and improving recall by 19.2%. We also evaluated the performance of the proposed matching algorithms that serve as the cornerstone of refactoring detection. The evaluation results suggested that the proposed algorithms excel in matching code entities, substantially reducing the number of mistakes (false positives plus false negatives) by 67% compared to the state-of-the-art approaches. © 2025 IEEE.

关键词： Software quality

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Rethinking the Design of Backdoor Triggers and Adversarial Perturbations: A Color Space Perspective

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2823-2840页

作者： Jiang, Wenbo Li, Hongwei Xu, Guowen Ren, Hao Yang, Haomiao Zhang, Tianwei Yu, Shui University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Sichuan University School of Cyber Science and Engineering Chengdu610017 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore University of Technology Sydney School of Computer Science in the Faculty of Engineering and Information Technology SydneyNSW2007 Australia

Deep neural networks (DNNs) are known to be susceptible to various malicious attacks, such as adversarial and backdoor attacks. However, most of these attacks utilize additive adversarial perturbations (or backdoor triggers) within an Lp -norm constraint. They can be easily defeated by image preprocessing strategies, such as image compression and image super-resolution. To address this limitation, instead of using additive adversarial perturbations (or backdoor triggers) in the pixel space, this work revisits the design of adversarial perturbations (or backdoor triggers) from the perspective of color space and conducts a comprehensive analysis. Specifically, we propose a color space backdoor attack and a color space adversarial attack where the color space shift is used as the trigger and perturbation. To find the optimal trigger or perturbation in the black-box scenario, we perform an iterative optimization process with the Particle Swarm Optimization algorithm. Experimental results confirm the robustness of the proposed color space attacks against image preprocessing defenses as well as other mainstream defense methods. In addition, we also design adaptive defense strategies and evaluate their effectiveness against color space attacks. Our work emphasizes the importance of the color space when developing malicious attacks against DNN and urges more research in this area. © 2004-2012 IEEE.

关键词： Image Color Analysis Perturbation Methods Image Preprocessing Robustness Training Image Coding Predictive Models Optimization Loss Measurement Iterative Algorithms Backdoor Attack Adversarial Attack Defense Mechanisms Image Color Space Color Components Adversarial Perturbations Backdoor Trigger Deep Neural Network Optimization Algorithm Super Resolution Particle Swarm Particle Swarm Optimization Defense Strategy Image Preprocessing Malicious Attacks Image Compression Adversarial Attacks Norm Constraint Adaptive Defense Objective Function Baseline Values Alternative Models Results In Table Data Augmentation Benign Samples Attack Success Rate Color Images Adversarial Examples Projected Gradient Descent Physical World Penalty Term Peak Signal To Noise Ratio Attack Performance Neural Architecture Search

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A Path-Based Topology-Agnostic Fault Diagnosis Strategy for Multiprocessor Systems

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IEEE Transactions on computers 2025年第6期74卷 1886-1896页

作者： Chen, Lin Feng, Hao Wu, Jiong Hainan University School of Computer Science and Technology Haikou570100 China

Fault diagnosis technology is a method for locating faulty processors in multiprocessor systems, and it plays a crucial role in ensuring system stability, security and reliability. A widely used approach in this technology is the system-level strategy, which determines processor status by interpreting the set of test results between adjacent processors. Among them, the PMC and MMmodels are two commonly employed methods for generating these results. The diversity and complexity of network topologies in systems constrain existing algorithms to specific topologies, while the limitations of fault diagnosis strategies lead to reduced fault tolerance. In this paper, we present a novel path-based method to tackle the fault diagnosis problems in various networks according to the PMC and MM models. Firstly, we introduce the algorithm for partitioning the path into subpaths based on these models. To ensure that at least one subpath is diagnosed as fault free, we derive the relationship between the fault bound T and the path length N. Then, building on methods for recognizing the subpath states, we have developed fault diagnosis algorithms for both the PMC and MM models. The simulation results show that our proposed algorithms can quickly and accurately diagnose faults in multiprocessor systems. © 1968-2012 IEEE.

关键词： Multiprocessing systems

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Disentangled Multi-Graph Convolution for Cross-Domain Recommendation

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ACM Transactions on Knowledge Discovery from Data 2025年第3期19卷 1-28页

作者： Gao, Yibo Liu, Zhen Yang, Xinxin Lu, Sibo Yuan, Yafan School of Computer Science and Technology Beijing Jiaotong University Beijing China

Data sparsity poses a significant challenge for recommendation systems, prompting the research of Cross-Domain Recommendation (CDR). CDR aims to leverage more user-item interaction information from source domains to improve the recommendation performance in the target domain. However, a major challenge in CDR is the identification of transferable features. Traditional CDR methods struggle to distinguish between the various features of users, including domain-invariant features that are effective for feature transfer and domain-specific features that are detrimental to cross-domain information transfer. In this article, we aim to disentangle domain-invariant features and domain-specific features and effectively utilize these different features. This enables effective domain-to-domain information transfer by only transferring domain-invariant features while still considering the role of domain-specific features within their respective domains. Based on the superiority of graph structural feature learning and disentangled represent learning, we propose - a model that learns Disentangled user feature representations and constructs a Multi-Graph network for bidirectional knowledge transfer of shared features for CDR. Specifically, we designed two regularization terms to disentangle domain-invariant features and domain-specific features. Subsequently, we established a multi-graph convolutional network to enhance domain-specific features within single-domain graphs and transfer domain-invariant features across cross-domain graphs. Our approach also includes designing feature constraints to enhance the combination of features derived from different graphs and to uncover potential correlations among them. Extensive experiments on real-world datasets have demonstrated that our model significantly outperforms state-of-the-art CDR approaches. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Contrastive Learning

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14-Bit SAR ADC with on-Chip Digital Bubble Sorting Calibration technology

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Chinese Journal of Electronics 2025年第1期34卷 125-136页

作者： Hua Fan Zhuorui Chen Tongrui Xu Franco Maloberti Qi Wei Quanyuan Feng School of Integrated Circuit Science and Engineering(Exemplary School of Microelectronics) University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chongqing Institute of Microelectronics Industry Technology University of Electronic Science and Technology of China Department of Electrical Computer and Biomedical Engineering University of Pavia Department of Precision Instrument Tsinghua University Institute of Microelectronics Southwest Jiaotong University

This article designs a 14-bit successive approximation register analog-to-digital converter（SAR ADC）.A novel digital bubble sorting calibration method is proposed and applied to eliminate the effect of capacitor mismatch on the linearity of the SAR ADC. To reduce the number of capacitors, a hybrid architecture of a high 8-bit binary-weighted capacitor array and a low 6-bit resistor array is adopted by the digital-to-analog（DAC）. The common-mode voltage VCM-based switching scheme is chosen to reduce the switching energy and area of the DAC. The time-domain comparator is employed to obtain lower power consumption. Sampling is performed through a gate voltage bootstrapped switch to reduce the nonlinear errors introduced when sampling the input signal. Moreover, the SAR logic and the whole calibration is totally implemented on-chip through digital integrated circuit（IC） tools such as design compiler, IC compiler, etc. Finally, a prototype is designed and implemented using 0.18 μm bipolar-complementary metal oxide semiconductor（CMOS）-double-diffused MOS 1.8 V CMOS technology. The measurement results show that the SAR ADC with on-chip bubble sorting calibration method achieves the signal-to-noise-and-distortion ratio of 69.75 dB and the spurious-free dynamic range of 83.77 dB.

关键词： Semiconductor device measurement Capacitors Linearity Switches Calibration System-on-chip Frequency measurement Time-domain analysis Sorting Signal to noise ratio

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Modeling and Performance Evaluation of Streaming Data Processing System in IoT Architecture

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computers, Materials & Continua 2025年第5期83卷 2573-2598页

作者： Feng Zhu Kailin Wu Jie Ding School of Computer Jiangsu University of Science and TechnologyZhenjiang212100China

With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of *** distributed streaming data processing frameworks such asApache Flink andApache Spark Streaming provide solutions,meeting stringent response time requirements while ensuring high throughput and resource utilization remains an urgent *** address this,the study proposes a formal modeling approach based on Performance Evaluation Process Algebra(PEPA),which abstracts the core components and interactions of cloud-based distributed streaming data processing ***,a generic service flow generation algorithmis introduced,enabling the automatic extraction of service flows fromthe PEPAmodel and the computation of key performance metrics,including response time,throughput,and resource *** novelty of this work lies in the integration of PEPA-based formal modeling with the service flow generation algorithm,bridging the gap between formal modeling and practical performance evaluation for IoT *** experiments demonstrate that optimizing the execution efficiency of components can significantly improve system *** instance,increasing the task execution rate from 10 to 100 improves system performance by 9.53%,while further increasing it to 200 results in a 21.58%***,diminishing returns are observed when the execution rate reaches 500,with only a 0.42%***,increasing the number of TaskManagers from 10 to 20 improves response time by 18.49%,but the improvement slows to 6.06% when increasing from 20 to 50,highlighting the importance of co-optimizing component efficiency and resource management to achieve substantial performance *** study provides a systematic framework for analyzing and optimizing the performance of IoT systems for large-scale real-time streaming data processing.

关键词： System modeling performance evaluation streaming data process IoT system PEPA

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Advancing a thriving materials science community: the 2025 Emerging Investigator Issue of science china Materials

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science china Materials 2025年第3期68卷 689-690页

作者： Yi Xie School of Chemistry and Materials Science University of Science and Technology of ChinaHefei 230026China

To support early-career scientists in the initial stages of their independent careers,science china Materials is proud to present the 2025 Emerging Investigator *** initiative emphasizes the recognition of emerging re... 详细信息

关键词： Issue career recognition

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A Semantic-Based Approach for Self-Configuring Smart Home Systems Using Feedback-Based Reinforcement Learning

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IEEE Internet of Things Journal 2025年第11期12卷 17729-17741页

作者： Ran, Weizhi Nantogma, Sulemana Yang, QingZhuo Zhang, Shangyan Wang, Yan Xu, Yang University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Xi'an University of Architecture and Technology School of Information and Control Engineering China

As urban populations grow, smart home technology has become a key enabler for enhancing energy efficiency, comfort, and convenience in residential environments. However, existing smart home implementations often struggle to adapt to complex and dynamic user needs due to issues like device heterogeneity, rigid pre-set solutions, and limited interoperability. This paper presents a novel semantic-based self-configuring framework that captures the functional, operational, state, and characteristic perspectives of smart home devices, facilitating seamless interconnection and interoperability across heterogeneous devices. The proposed framework uses service entity, that is semantically modeled to act as a linker and mediator between applications and devices to enable dynamic scheduling and control based on application requests and user preferences. To enable continuous adaptation and learning, the proposed approach formulates the self-configuration problem as a Markov Decision Process (MDP), with parameters instantiated using semantic information and rules. A novel Feedback-based Reinforcement Learning approach is then proposed to adjust the reward function and state transition parameters based on user feedback. The performances of the proposed approach against a bench-marked fixed and rule-based mechanisms are demonstrated through simulation. © 2014 IEEE.

关键词： Smart homes

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Entity and Relationship Extraction with High-quality Spans and Enhanced Marker Strategies

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Machine Intelligence Research 2025年第2期22卷 289-303页

作者： Qibin Li Nianmin Yao Nai Zhou School of Computer Science Dalian University of TechnologyDalian 116024China Ningbo Institute of Dalian University of Technology Ningbo 116026China

Entity and relation extraction is a critical task in information *** approaches have emphasized obtaining improved span ***,existing work suffers from two major ***,there is an overabundance of low-quality candidate spans,which hinders the effective extraction of information from high-quality candidate ***,the information encoded by existing marker strategies is often too simple to fully capture the nuances of the span,resulting in the loss of potentially valuable *** address these issues,we propose an enhancing entity and relation extraction with high-quality spans and enhanced marker(HSEM)strategies,it assigns adaptive weights to different spans in order to make the model more focused on high quality ***,the HSEM model enriches marker representation to incorporate more span information and enhance entity ***,we design a span scoring framework that assesses span quality based on the fusion of internal information and focuses the model on training high-quality samples to improve *** results on six benchmark datasets demonstrate that our model achieves state-of-the-art results after discriminating span quality.

关键词： High-quality spans enhanced marker span scoring framework span representations entity and relation extraction

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Semi-Supervised Skin Lesion Segmentation Based on Pseudo-Labels

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第2期52卷 325-332页

作者： Mu, Bo Wei, JingXin Zhang, Yujun School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In recent years, deep learning has significantly advanced skin lesion segmentation. However, annotating medical image data is specialized and costly, while obtaining unlabeled medical data is easier. To address this challenge, we propose a semi-supervised learning strategy to improve segmentation accuracy by combining a small amount of annotated data with a larger volume of unlabeled data. Our approach employs a teacher-student model framework. In this framework, the teacher model generates pseudo-labels for the unlabeled data, and the student model is trained using both these pseudo-labels and the limited true labels. To improve the student model’s learning capacity, we introduce auxiliary segmentation heads that provide joint guidance during training. We use the crossentropy (CE) loss function to quantify the discrepancies between the segmentation outputs of the main head and auxiliary heads. Since pseudo-labels generated by the teacher model may contain noise, we developed a mechanism to identify and exclude uncertain regions in each unlabeled image. This reduces pseudolabel noise and mitigates its negative impact on the student model. Our method demonstrates significant improvements in skin lesion segmentation on the publicly available ISIC2018 dataset, achieving Dice coefficients of 87.84% and 88.73% with only 5% and 10% of the total annotated data, respectively, outperforming existing methods. © (2025), (International Association of Engineers). All rights reserved.

关键词： Self-supervised learning

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