检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Yu, Qixian Cao, Zining Hui, Zong Zhou, Yuan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing211106 China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing210023 China Faculty of Computer and Software Engineering Huaiyin Institute Of Technology Huaian223001 China

This paper presents a comprehensive framework for modeling and verifying multi-agent systems. The paper introduce an Epistemic Process Calculus for multi-agent systems, which formalizes the syntax and semantics to capture the essential features of agent behavior interactions and epistemic states. Building upon this calculus, we propose ATLE, an extension of Alternating-time Temporal Logic incorporating epistemic operators to express complex properties related to agent epistemic state. To verify ATLE specifications, this paper presents a model checking algorithm that systematically explores the state space of a multi-agent system and evaluates the satisfaction of the specified properties. Finally, a case study is given to demonstrate the method. © 2025, CC BY.

关键词： Multi agent systems

来源：评论

学校读者我要写书评

暂无评论

Code line generation based on deep context-awareness of onsite programming

引用

Science China(Information Sciences) 2020年第9期63卷 64-66页

作者： Chuanqi TAO Panpan BAO Zhiqiu HUANG College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing University of Aeronautics and Astronautics National Key Laboratory for Novel Software Technology Nanjing University

Dear editor,Intelligent code generation has become an essential research task to accelerate modern software development. To facilitate effective code generation for programming languages, numerous approaches have been... 详细信息

关键词： Onsite Programming code line deep context-awareness quality assurance re-ranking

来源：评论

学校读者我要写书评

暂无评论

MT4ImgRec: A metamorphic testing tool for image recognition software 33

MT4ImgRec: A metamorphic testing tool for image recognition ...

引用

33rd International Conference on software Engineering and Knowledge Engineering, SEKE 2021

作者： Cao, Dongyu Guo, Hongjing Tao, Chuanqi College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)1891706527

Although data-driven image recognition software has widely emerged in various fields, they suffer from quality issues. Metamorphic testing has been successfully applied to AI software for alleviating test oracle problems. Nevertheless, metamorphic testing still relies on manual methods in most cases, which is time-consuming. To improve test efficiency, a testing tool called MT4ImgRec is designed to automatically perform metamorphic testing for image recognition software. © 2021 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Efficient adaptive test case selection for DNNs robustness enhancement

引用

Journal of Systems and software 2025年 229卷

作者： Zhiyi Zhang Huanze Meng Yuchen Ding Shuxian Chen Yongming Yao Collage of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing Jiangsu China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing Jiangsu China School of Mathematical Science Yangzhou University Yangzhou Jiangsu China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Nanjing Jiangsu China Army Engineering of PLA Nanjing Jiangsu China

Deep neural networks (DNNs) have been widely used in various fields, and testing for DNN-based software has become increasingly important. To discover potential faults in DNNs, a large number of test cases and their corresponding labels are required. However, labeling so many test cases consumes enormous costs. Although there have been many test case selection techniques for DNN models, these techniques still have problems such as high overhead, low efficiency, and poor diversity. To address this problem, this paper proposes an efficient adaptive test case selection method based on the principle of uniform distribution of test cases called EATS. Based on the idea of adaptive testing, EATS combines the uncertainty of the model and the diversity of faults to calculate the distance of test cases and sort them, then gives priority to test cases with a higher probability of causing faults. We conduct experiments on four popular datasets and four representative DNN models. Experiment results show that, compared with the existing eight methods, EATS performs better in uniformity of test case distribution, diversity of errors found, model optimization, and optimization efficiency.

关键词：

来源：评论

学校读者我要写书评

暂无评论

software Defect Prediction Model Based on Syntactic Semantics and Flow Information Features 11

Software Defect Prediction Model Based on Syntactic Semantic...

引用

11th International Conference on Dependable Systems and Their Applications, DSA 2024

作者： Huang, Chongyang Zhu, Yi Yu, Qiao Ding, Yi Hao, Guosheng Jiangsu Normal University Jiangsu Xuzhou China Nanjing University of Aeronautics and Astronautics Key Laboratory for Safety-Critical Software Development and Verification Jiangsu Nanjing China

ISBN: (纸本)9798331532390

Using deep learning to determine whether a source code file contains defects has become an important research topic. In the past, many researchers have tended to convert code into Abstract Syntax Tree and use deep neural networks to learn the underlying syntactic and semantic information. However, this approach often overlooks the rich flow information embedded in the code, such as control flow. To address this issue, this paper proposes a RGCN-CNN model and applies it to software defect prediction. Specifically, we convert the source code into an Abstract Syntax Tree and a Code Property Graph. Then, we use Convolutional Neural Networks and Relational Graph Convolutional Networks to learn syntactic-semantic features and flow information features from the AST and CPG, respectively. Finally, the two learned features are concatenated and used to train a defect classifier. Experimental results show that the RGCN-CNN model can effectively identify defect files in software projects. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Understanding Security Issues based on App Comment Analysis 9

Understanding Security Issues based on App Comment Analysis

引用

9th International Conference on Dependable Systems and Their Applications, DSA 2022

作者： Chen, Mengyao Tao, Chuanqi Guo, Hongjing College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Nanjing University of Aeronautics and Astronautics Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing China Nanjing University National Key Laboratory for Novel Software Technology Nanjing China

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

Mobile Applications (App) security issues occur in sync with the progress of information technology. User comments serve as a valuable source of information for evaluating a mobile app, for both new users and developers. However, previous works rarely rely on existing comments to provide a detailed summarization of the app's security issues. We present a novel comment summarization framework, SBR-Miner(Security Bug Related-Comment Miner). It first extracts the security-related sentences from the comment sentences using a classification model, then extracts the aspect, opinion, misbehavior of the sentences using a deep learning model, and assigns security issues categories to the triads, and finally visualizes the comment summaries using a radar plot. Our evaluation of the manually labeled dataset shows that SBR-Miner can provide a clearer and more comprehensive aspect than the state-of-the-art, with an average F1 score of 0.92, higher than AR-Miner (0.80) and SUR-Miner (0.81). 92% of developers agree that the SBR-Miner summaries are beneficial, according to feedback from relevant practitioners. © 2022 IEEE.

关键词： Miners

来源：评论

学校读者我要写书评

暂无评论

DeepTD: Diversity-Guided Deep Neural Network Test Generation 9th

DeepTD: Diversity-Guided Deep Neural Network Test Generation

引用

9th International Symposium on Dependable software Engineering: Theories, Tools and Applications, SETTA 2023

作者： Zhu, Jin Tao, Chuanqi Guo, Hongjing Ju, Yue College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing China State Key Laboratory for Novel Software Technology Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China

ISBN: (纸本)9789819986637

Coverage-guided Fuzz Testing (CGF) techniques have been applied to deep neural network (DNN) testing in recent years, generating a significant number of test samples to uncover inherent defects in DNN models. However, the effectiveness of CGF techniques that utilize structured coverage metrics as coverage criteria is currently being questioned. A few unstructured coverage metrics, such as surprise adequacy, only take into account the diversity of the test samples against the training set, while ignoring the diversity of the test samples themselves. In addition to this, the existing surprise adequacy metrics have some limitations in their applications. Therefore, this paper proposes DeepTD, a diversity-guided deep neural networks test generation method. Firstly, DeepTD selects high-loss test samples from each class on average, ensuring these test seeds possess a strong ability to reveal model errors. Then, DeepTD transforms these test seeds to enhance the diversity of the generated samples. Finally, Cluster-based Surprise Adequacy is designed to guide the generation of test samples. To evaluate the effectiveness of DeepTD, six DNN models are selected as subjects, covering two well-known image datasets. Experimental results demonstrate that the Cluster-based Surprise Adequacy outperforms the two existing metrics not only in computational efficiency but also in discovering more model defects. What’s more, the test samples generated by DeepTD are on average 6.04% and 3.24% more effective for model retraining in MNIST and CIFAR10 compared to baseline methods, respectively. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Coverage-guided fuzz testing Deep neural network Test generation Test samples diversity

来源：评论

学校读者我要写书评

暂无评论

Metamorphic Testing for Plant Identification Mobile Applications Based on Test Contexts 11th

Metamorphic Testing for Plant Identification Mobile Applicat...

引用

11th International Conference on Mobile Computing, Applications, and Services, MobiCASE 2020

作者： Guo, Hongjing Tao, Chuanqi Huang, Zhiqiu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211100 China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Nanjing211100 China National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

ISBN: (纸本)9783030642136

With the fast growth of artificial intelligence and big data technologies, AI-based mobile apps are widely used in people’s daily life. However, the quality problem of apps is becoming more and more prominent. Many AI-based mobile apps often demonstrate inconsistent behaviors for the same input data when context conditions are changed. Nevertheless, existing work seldom focuses on performing testing and quality validation for AI-based mobile apps under different context conditions. To automatically test AI-based plant identification mobile apps, this paper introduces TestPlantID, a novel metamorphic testing approach based on test contexts. First, TestPlantID constructs seven test contexts for mimicking contextual factors of plant identification usage scenarios. Next, TestPlantID defines test-context-based metamorphic relations for performing metamorphic testing to detect inconsistent behaviors. Then, TestPlantID generates follow-up images with various test contexts for testing by applying image transformations and photographing real-world plants. Moreover, a case study on three plant identification mobile apps shows that TestPlantID could reveal more than five thousand inconsistent behaviors, and differentiate the capability of detecting inconsistent behaviors with different test contexts. © 2020, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： software testing

来源：评论

学校读者我要写书评

暂无评论

software Defect Prediction via GCN based on Structural and Context Information 9

Software Defect Prediction via GCN based on Structural and C...

引用

9th International Conference on Dependable Systems and Their Applications, DSA 2022

作者： Tang, Lijin Tao, Chuanqi Guo, Hongjing Zhang, Jingxuan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Nanjing University of Aeronautics and Astronautics Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

software quality plays an important role in the software engineering. To improve software reliability, software defect prediction which predicts code region is buggy or not aims to assist developers find bugs and allocate their test resources reasonably. Traditional defect prediction studies focus on handcrafted features and train machine learning classifiers to identify the defective code area. Since the source code contains rich semantic and structural information, and defective code is closely related to its context, now many researchers try to utilize semantic information of programs and build more accurate defect prediction model. Existing methods mainly analyze Abstract Syntax Tree (AST) which is obtained from source code and transform it into token sequences. But these methods fail to capture rich information of structural and context information of code. At the same time, some traditional features(e.g. The number of methods in the class(WMC), Lines of code(LOC)) are also very valuable in defect prediction. Therefore in this paper, we propose to a method to build the graph by connecting parent and child nodes of the AST and connecting leaf nodes from left to right to preserve the context information that contained in source code. Then we use Graph Convolutional Network(GCN) to automatically generate semantic and structural features from source code. In addition, we combine GCN learned features with handcrafted features for more accurate prediction model. Based on the above model, our evaluation on 12 data sets shows that our proposed method is superior to the state-of-the-art software defect prediction method by 10.37 % in recall, 1.62 % in precision, 5.15% in F-measure, and 8.11 % in MCC. © 2022 IEEE.

关键词： software reliability

来源：评论

学校读者我要写书评

暂无评论

A case study of testing an image recognition application 33

A case study of testing an image recognition application

引用

33rd International Conference on software Engineering and Knowledge Engineering, SEKE 2021

作者： Tao, Chuanqi Cao, Dongyu Guo, Hongjing Gao, Jerry College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Nanjing China Department of Computer Engineering San Jose State University United States

ISBN: (纸本)1891706527

High-quality Artificial intelligence (AI) software in different domains, like image recognition, has been widely emerged in our lives. They are built on machine learning models to implement intelligent features. However, the current research on image recognition software rarely discusses test questions, clear quality requirements, and verification methods. This paper presents a case study of a realistic image recognition application called Calorie Mama using manual and automation testing with a 3D decision table. The study results indicate the proposed method is feasible and effective in quality evaluation. © 2021 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Image recognition

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：