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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Zhi Chen Lingxiao Jiang Centre for Research on Intelligent Software Engineering School of Computing and Information Systems Singapore Management University

ISBN: (数字)9798331535100

ISBN: (纸本)9798331535117

In recent years, AI-based software engineering has progressed from pre-trained models to advanced agentic workflows, with software Development Agents representing the next major leap. These agents, capable of reasoning, planning, and interacting with external environments, offer promising solutions to complex software engineering tasks. However, while much research has evaluated code generated by large language models (LLMs), comprehensive studies on agent-generated patches, particularly in real-world settings, are lacking. This study addresses that gap by evaluating 4,892 patches from 10 top-ranked agents on 500 real-world GitHub issues from SWE-Bench Verified, focusing on their impact on code quality. Our analysis shows no single agent dominated, with 170 issues unresolved, indicating room for improvement. Even for patches that passed unit tests and resolved issues, agents made different file and function modifications compared to the gold patches from repository developers, revealing limitations in the benchmark's test case coverage. Most agents maintained code reliability and security, avoiding new bugs or vulnerabilities; while some agents increased code complexity, many reduced code duplication and minimized code smells. Finally, agents performed better on simpler codebases, suggesting that breaking complex tasks into smaller sub-tasks could improve effectiveness. This study provides the first comprehensive evaluation of agent-generated patches on real-world GitHub issues, offering insights to advance AI-driven software development.

关键词： Gold Codes Large language models Computer bugs Focusing Complexity theory Security Reliability software development management software engineering

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Automatic Multi-Class Non-Functional software Requirements Classification Using Machine Learning Algorithms

Automatic Multi-Class Non-Functional Software Requirements C...

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International Research Conference on Smart computing and Systems engineering (SCSE)

作者： M. A. F. Saroth P. M. A. K. Wijerathne B. T. G. S. Kumara Department of Computing and Information Systems Faculty of Computing Sabaragamuwa University of Sri Lanka Department of Software Engineering Faculty of Computing Sabaragamuwa University of Sri Lanka

ISBN: (数字)9798350375688

ISBN: (纸本)9798350375695

A successful software development process depends on software requirements. Those requirements are often classified into two categories: functional requirements (FR) and non-functional requirements (NFR). In software development, NFRs play a crucial role, but they are often forgotten by the developers. Therefore, it causes adverse consequences. As a result, an NFR classification method is required to assist software developers in classifying NFR from requirement documents. The majority of research in this area focuses on categorizing requirements as FR or NFR. However, NFR can be divided into more than two categories. As a result, a multi-label categorization strategy was required. This study presented an approach for categorizing NFR based on product, process, and external factors. To categorize the NFR, the authors developed four machine learning-based algorithms entitled Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and K Nearest Neighbor Classifier (KNN) classifier, as well as one deep learning method named Long Short-Term Memory (LSTM) model. Ultimately, the authors examine the models and conclude that the LSTM model which gained an accuracy of 99.69% is appropriate for this type of classification.

关键词： Support vector machines Deep learning Logistic regression Machine learning algorithms Computational modeling software Classification algorithms

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Deep Learning Algorithm for Person Re-Identification Based on Dual Network Architecture

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Computers, Materials & Continua 2025年第5期83卷 2889-2905页

作者： Meng Zhu Xingyue Wang Honge Ren Abeer Hakeem Linda Mohaisen College of Information Engineering Harbin UniversityHarbin150086China Heilongjiang Provincial Key Laboratory of the Intelligent Perception and Intelligent Software Harbin UniversityHarbin150086China College of Computer and Control Engineering Northeast Forestry UniversityHarbin150040China Heilongjiang Forestry Intelligent Equipment Engineering Research Center Northeast Forestry UniversityHarbin150040China Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia

Changing a person’s posture and low resolution are the key challenges for person re-identification(ReID)in various deep learning *** this paper,we introduce an innovative architecture using a dual attention network that includes an attentionmodule and a joint measurement module of spatial-temporal *** proposed approach can be classified into two main ***,the spatial attention feature map is formed by aggregating features in the spatial ***,the same operation is carried out on the channel dimension to formchannel attention ***,the receptive field size is adjusted adaptively tomitigate the changing person posture ***,we use a joint measurement method for the spatial-temporal information to fully harness the data,and it can also naturally integrate the information into the visual features of supervised ReID and hence overcome the low resolution *** experimental results indicate that our proposed algorithm markedly improves the accuracy in addressing changing human postures and low-resolution issues compared with contemporary leading *** proposed method shows superior outcomes on widely recognized benchmarks,which are the Market-1501,MSMT17,and DukeMTMC-reID ***,the proposed algorithmattains a Rank-1 accuracy of 97.4% and 94.9% mAP(mean Average Precision)on the Market-1501 ***,it achieves a 94.2% Rank-1 accuracy and 91.8% mAP on the DukeMTMC-reID dataset.

关键词： Person reidentification ReID computer vision self-attention spatial-temporal information

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Quantum Machine Learning for Security Data Analysis

Quantum Machine Learning for Security Data Analysis

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2023 IEEE World AI IoT Congress, AIIoT 2023

作者： Bandi, Dhanunjai Shi, Yong Shahriar, Hossain Lo, Dan Suo, Kun Chi, Hongmei Qian, Kai Kennesaw State University College of Computing and Software Engineering United States Florida A&M University Department of Computer and Information Sciences TallahasseeFL United States

ISBN: (纸本)9798350337617

In this paper, we apply Quantum Machine Learning to analyze security datasets. We compare cross-models, Quantum Machine Learning (QML) against Classical Machine Learning (CML), performance with increasing data size, and performance with increasing iteration numbers using commonly used machine learning techniques such as Neural Networks (NN), Support Vector Machines (SVM), and Logistic Regression (LR). Our study focuses on assessing the accuracy of QML and CML approaches on real-world security datasets. The results provide light on the advantages and disadvantages of both QML and CML methodologies, with implications for their use in security data analysis. The experimental findings provide useful information on the applicability of QML and CML for security-related applications. The study contributes to the growing field of quantum machine learning research, particularly in the context of security data analysis, and offers helpful advice for academics and practitioners working in this area. © 2023 IEEE.

关键词： Support vector machines

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Non-IT Program Students Learn Basic Web Language;The Preliminary Study of Affective and Cognitive Response on IVML Prototype 17th

Non-IT Program Students Learn Basic Web Language;The Prelimi...

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17th International Conference on Information Technology and Applications, ICITA 2023

作者： Naim, Nur Faraha Mohd Ibrahim, Ag. Asri Ag. Baharum, Aslina Moung, Ervin Gubin Salimun, Carolyn Multimedia Technology Programme Faculty of Computing and Informatics Universiti Malaysia Sabah Kampus Antarabangsa Labuan Federal Territory of Labuan Malaysia Software Engineering Programme Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia User Experience Research Group Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia School of Engineering and Technology Sunway University Selangor Petaling Jaya Malaysia

ISBN: (纸本)9789819983230

This paper examines the response of affective and cognitive processes under human–computer interaction (HCI) toward interactive video mobile learning known as the IVML prototype. The idea is to determine the potential of the interactive elements in the educational video as the key factor that can influence learners’ engagement and comprehension of the presented topic. This paper aims to measure the level of affective and cognitive response of learners toward the subject learning that are not from their field, not exposed to, or not familiar with. The data analyze based on user experience, user interface, and human–computer interaction. The statistical result may produce a significant outcome that could contribute to the hyper video platform. The findings may confirm the significant role of technical aspects and psychological aspects included in video production style, hence heightening the hyper video platform in teaching and learning activities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Human computer interaction

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ESR: Optimizing Gene Feature Selection for scRNA-seq Data 10

ESR: Optimizing Gene Feature Selection for scRNA-seq Data

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10th IEEE International Conference on Cyber Security and Cloud computing and 9th IEEE International Conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Huang, Tao Chen, Xiang Peng, Li Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan Hunan411201 China Hunan Key Laboratory for Service computing and Novel Software Technology China

ISBN: (纸本)9798350312461

The rapid development of single-cell RNA sequencing (scRNA-seq) technology has enabled researchers to explore gene expression differences at the level of individual cells, revealing more refined cell types and states. However, due to the low expression and high noise of scRNA-seq data, feature selection has become particularly important in the analysis of single-cell data. Here, we introduce the Entropy Stepwise Regression (ESR) method for feature selection. This method utilizes the correlation between genes and the entropy values of each feature to filter out genes that are conducive to downstream analysis. In mouse kidney samples, we compared the performance of three methods in terms of Adjusted Rand Index and achieved good results. This indicates that the method can improve the accuracy of downstream analysis. © 2023 IEEE.

关键词： Entropy

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Mobile-YOLO-SDD: A Lightweight YOLO for Real-time Steel Defect Detection 18

Mobile-YOLO-SDD: A Lightweight YOLO for Real-time Steel Defe...

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18th CIRP Conference on Computer Aided Tolerancing, CAT 2024

作者： Luo, Shen Xu, Yuanping Zhu, Ming Zhang, Chaolong Kong, Chao Jin, Jin Li, Tukun Jiang, Xiangqian Guo, Benjun School of Software Engineering Chengdu University of Information Technology Chengdu China School of Automation Chengdu University of Information Technology Chengdu China School of Computing and Engineering University of Huddersfield HuddersfieldHD1 3DH United Kingdom

Defect detection is essential in the steel production process. Recent years have seen significant advancements in steel surface defect detection based on deep learning methods, notably exemplified by the YOLO series models capable of precise and rapid detection. However, challenges arise due to the high complexity of surface textures on steel and the low recognition rates for minor defects, making real-time and accurate detection difficult. This study introduces Mobile-YOLO-SDD (Steel Defect Detection), a lightweight YOLO-based model designed with high accuracy for real-time steel defect detection. Firstly, based on the effective YOLOv5 algorithm for steel defect detection, the backbone network is replaced with MobileNetV2 to reduce the model size and computational complexity. Then, the ECA (Efficient Channel Attention) module was integrated into the C3 module to reduce the number of parameters further while maintaining the defect detection rate in complex backgrounds. Finally, the K-Means++ algorithm regenerates anchor boxes and determines optimal sizes, enhancing their adaptability to actual targets. Experimental results on NEU-DET data demonstrate that the improved algorithm achieves a 60.6% reduction in model size, a 60.8% reduction in FLOPs, and a 1.8% improvement in mAP compared to YOLOv5s. These results confirm the effectiveness of Mobile-YOLO-SDD and lay the foundation for subsequent lightweight deployment of steel defect detection models. © 2024 The Authors. Published by Elsevier B.V.

关键词： Deep learning

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TH-SLP: Web Service Link Prediction Based on Topic-aware Heterogeneous Graph Neural Network

TH-SLP: Web Service Link Prediction Based on Topic-aware Het...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Peng, Qian Cao, Buqing Xie, Xiang Liu, Shanpeng Kang, Guosheng Liu, Jianxun Hunan University of Science and Technology Xiangtan School of Computer Science and Engineering Hunan Provincial Key Lab. for Services Computing and Novel Software Technology Hunan China

ISBN: (纸本)9798350304855

With the emergence of more and more Web services, finding suitable services becomes a difficult problem. Service link prediction is employed to disclose relationships among services, which facilitates the further development of service composition, selection, and recommendation. But the existing link prediction approaches simply utilize the structural features of the service network. In reality, the rich text content in service node description documents also carries latent but fine-grained semantics generated by multifaceted topic-aware factors, yet few efforts are committed to mining them. In this paper, we propose a Web service link prediction method based on a topic-aware heterogeneous graph neural network. Specifically, the method consists of two main layers, including the meta-path intra-decomposition and the meta-path inter-mergence. Meta-path intra-decomposition aims to mine the topic distribution of the meta-paths-based context while capturing fine-grained topic-aware semantics. Meta-path inter-mergence uniquely aggregates topic-aware factors according to the mined distribution and adopts a multifaceted attention mechanism to aggregate different meta-paths, enabling service nodes to generate multifaceted topic-aware embeddings that preserve not only the structure and but also the topic-aware semantics. In addition, a topic prior guidance regularization item is set up for quality assurance of multifaceted topic-aware embedding that depends on global knowledge of the unstructured text content in description documents. Experimental results on real datasets show that our proposed model outperforms other existing baselines methods in the link prediction task, successfully validating the effectiveness of our proposed method. © 2023 IEEE.

关键词： Semantics

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FreqSense: Universal and Low-Latency Adversarial Example Detection for Speaker Recognition with Interpretability in Frequency Domain

FreqSense: Universal and Low-Latency Adversarial Example Det...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Yihuan Li, Yuanzhe Ren, Yanzhen Tu, Weiping Yang, Yuhong Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education China School of Cyber Science and Engineering Wuhan University China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China

ISBN: (纸本)9798350368741

Speaker recognition (SR) systems are particularly vulnerable to adversarial example (AE) attacks. To mitigate these attacks, AE detection systems are typically integrated into SR systems. To overcome the limitations of low detection accuracy, poor generalization, and high latency in existing schemes, this paper proposes FreqSense, an AE detection scheme based on frequency distribution features. FreqSense detects a variety of unknown AE attacks with low latency, and provides interpretability in its detection process. The basic idea of FreqSense is that AE typically introduce carefully designed noise in specific frequency bands that are associated with highly distinctive speaker identities. Therefore, leveraging the distributional variations in these frequency bands can effectively distinguish between AE and benign audio. FreqSense models frequency distribution features by integrating time-frequency transformation technology with a self-attention mechanism and employs a neural network-based classifier to distinguish between AE and benign audio. Experimental results show that FreqSense achieves an overall detection accuracy of 99.2%, surpassing state-of-the-art (SOTA) schemes by 27.2%. When confronting unknown AE attacks, FreqSense achieves a detection accuracy of 98.3% with a latency of just 0.0014 seconds. © 2025 IEEE.

关键词： Adversarial Attack Adversarial Example Detection Speaker Recognition

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SUNET: Framework, Security, and Privacy

SUNET: Framework, Security, and Privacy

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2024 International Conference on Satellite Internet, SAT-NET 2024

作者： Xie, Zhan Wang, Liangmin Wu, Haiqin Zhang, Xiaoyu Wang, Liang Yang, Haotian Southeast University School of Cyber Science and Engineering Nanjing211189 China Southeast University Engineering Research Center of BASAM Ministry of Education Nanjing211189 China East China Normal University Software Engineering Institute Shanghai Key Laboratory of Trustworthy Computing Shanghai200062 China

ISBN: (纸本)9798350375176

SUNet (Satellite-assisted Unmanned-system Network) integrates satellite communication technology, unmanned equipment systems, and ground support facilities. Compared to traditional network system, SUNet has the advantages of flexible deployment and wide communication range. SUNet with cloud services (SUNET) breaks the limitation of satellites processing large-scale data, and integrates a large scope of computing and storage resources in a system. However, SUNET also poses more serious security risks than traditional networks for two reasons: i) the unique network framework, such as highly dynamic topology, unfixed network access points and decentralized control, of SUNET increases security risks, and ii) the rising probability of existing neighbor nodes with quantum computing. This paper firstly introduces the term SUNET from definition and compares SUNET with several related network frameworks. Then we anaylze the security risks, such as untrust between nodes, unauthorized access and quantum computing attack, faced by SUNET. Meanwhile, we illustrate the security requirements and the corresponding solutions, such as authentication, fine-grained authorization and quantum security, in SUNET. Finally, we discuss the security countermeasures and point out the potential future research directions. © 2024 IEEE.

关键词： Quantum computers

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