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IEEE International Conference on software Quality, Reliability and Security Companion (QRS-C)

作者： Lizhi Cai Naiqi Wang Mingang Chen Jin Wang Jilong Wang Jiayu Gong Shanghai Key Laboratory of Computer Software Testing & Evaluating Shanghai Development Center of Computer Software Technology Shanghai China School of Information Science and Engineering. East China University of Science and Technology Shanghai China

ISBN: (纸本)9781665478373

In recent years, a new testing method based on the concept of crowdsourcing has made great progress. Developers upload the project to the crowdsourcing test platform and recruit a large number of crowdsourcing workers for testing, so that the testing process has higher test adequacy, faster testing speed and lower testing cost. However, the test reports submitted after the test have serious problems such as large quantity and high similarity, resulting in the failure to achieve the expected results. Based on the method of feature fusion, this paper integrates the text description information, bug type information and screenshot information of crowdsourcing test reports, clusters crowdsourcing test reports through the calculation of similarity between reports, and finally achieves better results.

关键词： Crowdsourcing Costs Conferences Machine learning software quality Natural language processing Mathematical models

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Research progress in the processing of crowdsourced test reports 14

Research progress in the processing of crowdsourced test rep...

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14th EAI International Conference on Testbeds and Research Infrastructures for the Development of Networks and Communications, TridentCom 2019

作者： Wang, Naiqi Cai, Lizhi Chen, Mingang Zhang, Chuwei School of Information Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer Software Technology Shanghai China Shanghai Foreign Affairs Service Center Shanghai China

ISBN: (纸本)9783030432140

In recent years, crowdsourced testing, which uses collective intelligence to solve complex software testing tasks has gained widespread attention in academia and industry. However, due to a large number of workers participating in crowdsourced testing tasks, the submitted test reports set is too large, making it difficult for developers to review test reports. Therefore, how to effectively process and integrate crowdsourced test reports is always a significant challenge in the crowdsourced testing process. This paper deals with the crowdsourced test reports processing, sorts out some achievements in this field in recent years, and classifies, summarizes, and compares existing research results from four directions: duplicated reports detection, test reports aggregation and classification, priority ranking, and reports summarization. Finally explored the possible research directions, opportunities and challenges of the crowdsourced test reports. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering. 2020.

关键词： software testing

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FLSSM: A Federated Learning Storage Security Model with Homomorphic Encryption

arXiv

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arXiv 2025年

作者： Li, Yang Xia, Chunhe Li, Chang Li, Xiaojian Wang, Tianbo School of Computer Science and Engineering Beihang University Beijing100191 China Key Laboratory of Beijing Network Technology Beihang University Beijing100191 China Guangxi Collaborative Innovation Center Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin541004 China School of Computer Science and Technology Zhengzhou University of Light Industry Zhengzhou450000 China College of Computer Science and Information Technology Guangxi Normal University Guilin541004 China School of Cyber Science and Technology Beihang University Beijing100191 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China

Federated learning based on homomorphic encryption has received widespread attention due to its high security and enhanced protection of user data privacy. However, the characteristics of encrypted computation lead to three challenging problems: "computation-efficiency", "attack-tracing" and "contribution-assessment". The first refers to the efficiency of encrypted computation during model aggregation, the second refers to tracing malicious attacks in an encrypted state, and the third refers to the fairness of contribution assessment for local models after encryption. This paper proposes a federated learning storage security model with homomorphic encryption (FLSSM) to protect federated learning model privacy and address the three issues mentioned above. First, we utilize different nodes to aggregate local models in parallel, thereby improving encrypted models’ aggregation efficiency. Second, we introduce trusted supervise nodes to examine local models when the global model is attacked, enabling the tracing of malicious attacks under homomorphic encryption. Finally, we fairly reward local training nodes with encrypted local models based on trusted training time. Experiments on multiple real-world datasets show that our model significantly outperforms baseline models in terms of both efficiency and security metrics. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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BRFL: A Blockchain-based Byzantine-Robust Federated Learning Model

arXiv

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arXiv 2023年

作者： Li, Yang Xia, Chunhe Li, Chang Wang, Tianbo The Key Laboratory of Beijing Network Technology Beihang University Beijing100191 China The Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin541004 China The School of Computer Science and Technology Zhengzhou University of Light Industry Zhengzhou450000 China The School of Cyber Science and Technology Beihang University Beijing100191 China The Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China

With the increasing importance of machine learning, the privacy and security of training data have become critical. Federated learning, which stores data in distributed nodes and shares only model parameters, has gained significant attention for addressing this concern. However, a challenge arises in federated learning due to the Byzantine Attack Problem, where malicious local models can compromise the global model's performance during aggregation. This article proposes the Blockchainbased Byzantine-Robust Federated Learning (BRLF) model that combines federated learning with blockchain technology. This integration enables traceability of malicious models and provides incentives for locally trained clients. Our approach involves selecting the aggregation node based on Pearson's correlation coefficient, and we perform spectral clustering and calculate the average gradient within each cluster, validating its accuracy using local dataset of the aggregation nodes. Experimental results on public datasets demonstrate the superior byzantine robustness of our secure aggregation algorithm compared to other baseline byzantine robust aggregation methods, and proved our proposed model effectiveness in addressing the resource consumption problem. Copyright © 2023, The Authors. All rights reserved.

关键词： Blockchain

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FedDLM: A Fine-Grained Assessment Scheme for Risk of Sensitive Information Leakage in Federated Learning-based Android Malware Classifier

FedDLM: A Fine-Grained Assessment Scheme for Risk of Sensiti...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Changnan Jiang Chunhe Xia Chen Chen Huacheng Li Tianbo Wang Xiaojian Li Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Cyber Science and Technology Beihang University Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China College of Computer Science and Information Technology Guangxi Normal University Guilin China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

In the traditional centralized Android malware classification framework, privacy concerns arise as it requires collecting users’ app samples containing sensitive information directly. To address this problem, new classification frameworks based on Federated Learning (FL) have emerged for privacy preservation. However, research shows that these frameworks still face risks of indirect information leakage due to adversary inference. Unfortunately, existing research lacks an effective assessment of the extent and location of this leakage risk. To bridge the gap, we propose the FedDLM, which provides a fine-grained assessment of the risk of sensitive information leakage in an FL-based Android malware classifier. FedDLM estimates attackers’ theoretical maximum inference ability from the information theory perspective to gauge the degree of leakage risk in the classifier effectively. It precisely identifies critical positions in the shared gradient where the leakage risk exists by utilizing characteristics of class activation in classifiers. Through extensive experiments on the Androzoo dataset, FedDLM demonstrates its superior effectiveness and precision compared to baseline methods in evaluating the risk of sensitive information leakage. The evaluation results provide valuable insights into information leakage problems in classifiers and targeted privacy protection methods.

关键词： Meters Privacy Federated learning Information leakage Malware Security Risk management

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HF-Mid: A Hybrid Framework of Network Intrusion Detection for Multi-type and Imbalanced Data

HF-Mid: A Hybrid Framework of Network Intrusion Detection fo...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Weidong Zhou Tianbo Wang Guotao Huang Xiaopeng Liang Chunhe Xia Xiaojian Li Beijing Key Lab. of Network Technology Beihang University Beijing China School of Cyber Science and Technology Beihang University Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing Guangxi Normal University Guilin China College of Computer Science and Information Technology Guangxi Normal University Guilin China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

The data-driven deep learning methods have brought significant progress and potential to intrusion detection. However, there are two thorny problems caused by the characteristics of intrusion data: "multi-type features" and "data imbalance". The former means that forcefully and improperly transforming intrusion features from distinct metric spaces can result in semantic loss and noise. The latter indicates that the intrusion data is imbalanced in quantity and quality due to its complex spatial distribution. We propose a Hybrid Framework for Multi-type and Imbalance Data (HF-Mid) to address the above two problems. Firstly, we divide the intrusion features into equivalent and non-equivalent groups, and then embed them sequentially using Supervised Paragraph Vector-Distributed Memory (SPV-DM), which excels at modeling co-occurrence relationships, and Deep Neural Network (DNN), which is suitable for modeling non-linear relationships, thereby solving the "multitype features" problem. Secondly, we adopt a low-noise collective matrix factorization (CMF) model to fuse the two obtained features for dimensionality reduction. Finally, we employ a multiple classifier to detect intrusion. During the classifier training stage, we design a genetic algorithm-based proportional sampling method to select high-quality samples in each training batch. thus addressing the "data imbalance" problem. The experimental results demonstrate the proposed framework exhibits an overall improvement of 5.9% and 1.5% in terms of accuracy and false positive rate on average, respectively.

关键词： Training Privacy Fuses Noise Semantics Artificial neural networks Feature extraction

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Chemical reaction-inspired dual-population co-evolutionary algorithm for many-objective optimization

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Expert Systems with Applications 2025年 289卷

作者： Weichao Ding Mingshan Chen Wenbo Dong Fei Luo Chunhua Gu School of Information Science and Engineering East China University of Science and Technology 130 Meilong Road Shanghai 200237 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

Many-objective optimization problems (MaOPs) refer to the multi-objective problems with more than three conflicting objectives that need to be optimized simultaneously, which are prevalent but more challenging in scientific research and engineering applications. Existing many-objective optimization algorithms mainly make improvements in environmental selection strategies but rarely focus on the generation of offspring, and balancing convergence and diversity is still very difficult. To address these issues, this paper employs four basic collision operators in chemical reaction optimization to generate offspring and proposes a novel chemical reaction-inspired dual-population co-evolutionary algorithm (called DPCRO), which aims to further balance convergence and diversity to improve the quality of solutions in solving MaOPs. The proposed DPCRO consists of two independent and parallel populations, which are the uni-molecular evolutionary population (UE) and the bi-molecular evolutionary population (BE). UE adopts the uni-molecular collision operators for individual convergence optimization and provides convergence information for BE, while BE uses the bi-molecular collision operators for individual diversity optimization and provides diversity information for UE. These two populations work collaboratively to effectively balance their own convergence and diversity through collaborative interaction mechanisms. We conduct performance comparison experiments using multiple benchmark test problems with different characteristics. The experimental results show that the proposed algorithm not only enhances the performance of solving low-dimensional multi-objective problems, but also has strong competitiveness in addressing MaOPs.

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Construction of a Cloud Scenario Knowledge Graph for Cloud Service Market

Construction of a Cloud Scenario Knowledge Graph for Cloud S...

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IEEE International Conference on software Engineering and Service Sciences (ICSESS)

作者： Shiqi Zhang Xiaoqiang Liu Bo Xu Lizhi Cai Yun Hu School of Computer Science and Technology Donghua University & Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Software Center Shanghai China School of Computer Science and Technology Donghua University Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Software Center Shanghai China

ISBN: (数字)9781728165790

ISBN: (纸本)9781728165806

Fast-growing cloud computing technology makes cloud application scenarios more diversified and difficult to choose. A knowledge recommending system is an effective way to reach automatic recommendation services. In this paper, we constructed a cloud scenario knowledge graph (CSKG) for cloud service market to assist cloud knowledge management and provided a web service interface for recommended applications. First a cloud scenario domain ontology was defined based on product knowledge of cloud suppliers and experience of domain experts. Then the BiLSTM-CRF model and Rule-based method were combined to automatically extract structured knowledge from multi-source and heterogeneous texts, where we proposed a tagging schema combined with entity extraction and relation classifier. Finally, a cloud scenario knowledge graph was constructed and visualized, several application examples based on the CSKG service were suggested.

关键词： Knowledge engineering Cloud computing Visualization Web services Tagging Ontologies software engineering

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Dietary Sequence Recommendations Based on TransFM

Dietary Sequence Recommendations Based on TransFM

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International Conference on Smart Grid and Electrical Automation (ICSGEA)

作者： Jie Wu Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai P. R. China

ISBN: (数字)9781728182780

ISBN: (纸本)9781728182797

The rapid development of the Internet has changed people's lifestyles, and more and more people choose to eat online. The online catering platform for users inevitably needs to provide personalized recommendations for users, followed by fewer articles on dietary recommendations. This paper proposes a combination of GBDT and TransFM for dietary sequence recommendation. The hybrid model uses GBDT to process high-dimensional features of food data and capture high-order feature interactions, and then uses TransFM to combine the content features of food and mine the short-term interests of users. Perform sequence recommendations. After experimental comparison, the model is superior to the general sequence recommendation model, and has achieved good results on multiple performance indicators.

关键词： Zirconium Smart grids Hafnium Ground penetrating radar Geophysical measurement techniques Discrete wavelet transforms Conferences

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A differential evolution-based approach for effort-Aware just-in-Time software defect prediction 1

A differential evolution-based approach for effort-Aware jus...

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1st ACM SIGSOFT International Workshop on Representation Learning for software Engineering and Program Languages, RL+SE and PL 2020, co-located with ESEC/FSE 2020

作者： Yang, Xingguang Yu, Huiqun Fan, Guisheng Yang, Kang East China University of Science and Technology Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China East China University of Science and Technology Shanghai Engineering Research Center of Smart Energy Shanghai China East China University of Science and Technology Shanghai China

ISBN: (纸本)9781450381253

software defect prediction technology is an effective method to improve software quality. Effort-Aware just-in-Time software defect prediction (JIT-SDP) aims to identify more defective changes in limited effort. Although many methods have been proposed for JIT-SDP, the prediction performance of existing prediction models still needs to be improved. To improve the effort-Aware prediction performance, we propose a new method called DEJIT based on differential evolution algorithm. First, we propose a metric called density-percentile-Average (DPA), which is used as the optimization objective of models on the training set. Then, we use logistic regression to build models and use the differential evolution algorithm to determine coefficients of logistic regression. We conduct empirical research on six open source projects. Empirical results demonstrate that the proposed method significantly outperforms the state-of-The-Art 4 supervised models and 4 unsupervised models. © 2020 ACM.

关键词： Defects

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