检索结果-内蒙古大学图书馆

Towards Improving the Quality of Requirement and Testing Process in Agile software Development:An Empirical Study

computers, Materials & Continua 2024年第9期80卷 3761-3784页

作者： Irum Ilays Yaser Hafeez Nabil Almashfi Sadia Ali Mamoona Humayun Muhammad Aqib Ghadah Alwakid University Institute of Information Technology Pir Mehr Ali Shah Arid Agriculture UniversityRawalpindi46000Pakistan Department of Software Engineering College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia School of Arts Humanities and Social Sciences University of RoehamptonLondonSW155PJUK Department of Computer Science College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia

software testing is a critical phase due to misconceptions about ambiguities in the requirements during specification,which affect the testing ***,it is difficult to identify all faults in *** requirement changes continuously,it increases the irrelevancy and redundancy during *** to these challenges;fault detection capability decreases and there arises a need to improve the testing process,which is based on changes in requirements *** this research,we have developed a model to resolve testing challenges through requirement prioritization and prediction in an agile-based *** research objective is to identify the most relevant and meaningful requirements through semantic analysis for correct change *** compute the similarity of requirements through case-based reasoning,which predicted the requirements for reuse and restricted to error-based ***,the apriori algorithm mapped out requirement frequency to select relevant test cases based on frequently reused or not reused test cases to increase the fault detection ***,the proposed model was evaluated by conducting *** results showed that requirement redundancy and irrelevancy improved due to semantic analysis,which correctly predicted the requirements,increasing the fault detection rate and resulting in high user *** predicted requirements are mapped into test cases,increasing the fault detection rate after changes to achieve higher user ***,the model improves the redundancy and irrelevancy of requirements by more than 90%compared to other clustering methods and the analytical hierarchical process,achieving an 80%fault detection rate at an earlier ***,it provides guidelines for practitioners and researchers in the modern *** the future,we will provide the working prototype of this model for proof of concept.

关键词： Requirement prediction software testing agile software development semantic analysis case-based reasoning

来源：评论

学校读者我要写书评

暂无评论

Advanced Technological Improvements in Making of Smart Production Using AI and ML 4

Advanced Technological Improvements in Making of Smart Produ...

引用

4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Praveenraj, D. David Winster Vijayaraj, T. Kumar, K. Sambath Sakthivel, N.K. Multani, Fahim Nagaveni, P. Christ University School of Business and Management TN Bangalore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering TN India Department of Electronics and Communication Engineering Chennai Tamil Nadu Avadi India Nehru Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu T.M. Palayam India Ies College of Technology Department of Computer Science& Engineering M.P Bhopal India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering TN India

ISBN: (纸本)9798350360165

The necessity for adaptation and creativity in the manufacturing sector demonstrates the importance of sustainable manufacturing by the merging of advanced technologies. To encourage sustainability, a global view on the integration of smart manufacturing procedures is important. Artificial intelligence (or AI) has appeared as a crucial factor in achieving environmentally conscious manufacturing, with methods like the use of machine learning (ML) getting popularity. This study carefully studies the scientific papers related to the usage of AI and ML in business. The emergence of Industry 4.0 as a whole has positioned machine learning (ML) and artificial intelligence (AI) as drivers for the smart industry change. The study categorizes material based on release year, writers, scientific field, country, institution, and terms, applying the Web of Biology and SCOPUS databases. Utilize UCINET alongside NVivo 12 software, thereby the analysis covers empirical studies on machine learning (ML) and artificial intelligence (AI) via 1999 until the present, showing their growth before and after the start of Industry 4.0. Notably, the USA displays a substantial addition to this area, with a noticeable surge in desire following the rise of Industry 4.0. © 2024 IEEE.

关键词： Smart manufacturing

来源：评论

学校读者我要写书评

暂无评论

A Correlation And Order-Aware Rule Learning Method For Knowledge Graph Reasoning 29

A Correlation And Order-Aware Rule Learning Method For Knowl...

引用

29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： He, Yuefeng He, Yang Zheng, Xu Hui, Bei Tian, Bin University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China Naval University of Engineering Wuhan China

ISBN: (纸本)9798350330717

Mining high-quality logical rules is crucial as they can provide beneficial interpretability for predictions. Recent methods that incorporate logical rules into learning tasks have been proven to yield high-quality logical rules successfully. However, existing methods either rely on the rule instances observed to support rule mining, or simply embed the rule head and the rule body to learn from them. Additionally, they can not fully utilize the rich semantic information contained in logical rules and overlook the intrinsic correlations between all relations within the domain. In this paper, we propose a model called Correlation and order-Aware Rule Learning (CARL) that captures deeper semantic information in rules by allowing relations to be co-aware of each other and paying attention to logical sequence sensitivity. CARL utilizes semantic consistency between the rule body and rule head as its learning objective, continuously introducing more semantic information and logically simplifying the rule body while considering logical sequence sensitivity. We explored the internal correlations between domain relations and used the thought of knowledge distillation to simplify modules so that relations in CARL can share or perceive each other's information or state efficiently. Experiments on link prediction tasks have demonstrated that CARL can learn higher-quality rules and yield state-of-the-art results on four popular public datasets. https://***/burning5112/CARL © 2023 IEEE.

关键词： knowledge graph reasoning rule learning

来源：评论

学校读者我要写书评

暂无评论

A new online field feature selection algorithm based on streaming data

引用

Journal of Ambient Intelligence and Humanized Computing 2024年第2期15卷 1365-1377页

作者： Zhang, Zhenjiang Song, Fuxing Zhang, Peng Chao, Han-Chieh Zhao, Yingsi School of Software Engineering Beijing Jiaotong University Beijing100044 China Department of Electronic and Information Engineering Key Laboratory of Communication and Information Systems Beijing Municipal Commission of Education Beijing Jiaotong University Beijing100044 China School of Information Science and Engineering Fujian University of Technology Fuzhou350118 China School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan430023 China Department of Electrical Engineering National Dong Hwa University Hualien97401 Taiwan School of Economics and Management Beijing Jiaotong University Beijing100044 China

The rapid development of Internet technology derived out a massive network text data. Therefore, how to classify the massive text data efficiently has important theoretical significance and application value. In order to acquire accurate classification results, the process has been divided into two parts. In terms of text representation, this paper proposes an online field feature selection algorithm (OFFS algorithm) based on streaming data, which solves the problems of low efficiency and memory consumption of traditional feature selection algorithms. With improvements in the vector space model, the new algorithm can select the real-time feature of the data and quickly generate text vector. In the aspect of classifier design, an OFFS-BP neural network text classifier based on BP neutral network and OFFS algorithm is designed. It adapts to the distributed parallel computing, reduces the training time and balances the computation efficiency and classification accuracy. Finally based on the Spark platform, the OFFS-BP neural network classifier is implemented. The experimental results show that the OFFS-BP neural network classifier is more suitable for big data environment with less computation time and higher classification efficiency. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

A Unified Information Diffusion Prediction Model Based on Multi-task Learning 9th

A Unified Information Diffusion Prediction Model Based on M...

引用

19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Shang, Yingdan Zhou, Bin Zeng, Xiang Chen, Kai College of Computer Science and Technology National University of Defense Technology Changsha China Key Lab of Software Engineering for Complex Systems School of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9783031466731

The prediction of online information diffusion trends on social networks is crucial for understanding people’s interests and concerns, and has many real-world applications in fields such as business, politics and social security. Existing research on information diffusion prediction has predominantly focused on either macro level prediction of the future popularity of online information or micro level user activation prediction. However, information diffusion prediction on micro or macro level only may lead to one-sided prediction results, and there is a lack of research on implementing micro and macro level diffusion prediction tasks at the same time. Since micro and macro level diffusion prediction tasks are related to each other, we propose a unified information diffusion model which can jointly predicting the micro level user activation probability and macro level information popularity based on multi-task learning framework. We utilize graph neural network to learn user representation from both the information cascades and social network structure. Comparing with micro and macro level baseline methods separately, the prediction results of our proposed model outperform all baseline methods and proves the effectiveness of the proposed method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Towards Dynamic Evolutionary Analysis of ProgrammableWeb for API-Mashup Ecosystem 26

Towards Dynamic Evolutionary Analysis of ProgrammableWeb for...

引用

26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wang, Yang Xiang, Jiayan Cheng, Hangyu Chen, Wanjun Xiao, Yong Kang, Guosheng Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology Hnust Xiangtan China

ISBN: (纸本)9798350331684

With the wide adoption of Web APIs released on Internet, users tend to reuse them for business requirements or software development. Mashup is a useful technology for composing Web APIs into a new and value-added application. With the increasing number of Web APIs and Mashups, the API-Mashup ecosystem has emerged based on the invocation relationship between Mashups and Web APIs. In this paper, we take ProgrammableWeb, a typical API-Mashup ecosystem, as an example to investigate its dynamic evolutionary analysis. Although there have been some works on the API-Mashup ecosystem, they mainly focus on static analysis, i.e., the static characteristics of the API- Mashup ecosystem on a fixed time point. This paper conducts a comprehensive study on the dynamic evolutionary analysis of the API-Mashup ecosystem with a long time range from 2005 to 2021. First, we conduct a dynamic statistical analysis based on the API-Mashup ecosystem dataset. Next, we construct two cooperation networks, one between Web APIs, and the other between their categories. And the general characteristics of the two cooperation networks are presented. Finally, we investigate the derived cooperation networks from four perspectives: dynamic characteristics, degree distribution, betweenness centrality, and assortative mixing. Meanwhile, the corresponding insights are uncovered. Our work provides a foundation for visualization and understanding of the API-Mashup ecosystem from the timeline. © 2023 IEEE.

关键词： Ecosystems

来源：评论

学校读者我要写书评

暂无评论

Towards agile collaborative multi-view modeling with inconsistency tolerance

Towards agile collaborative multi-view modeling with inconsi...

引用

2024 Symposium on software Technologies: Applications and Foundations, STAF 2024 Workshops: 2nd International Workshop on Agile Model-Driven engineering, AgileMDE 2024, 1st Large Language Models for Model-Driven engineering Workshop, AgileMDE 2024 and 4th International Workshop on Model-Driven engineering for Smart Internet-of-Things Systems, MeSS 2024

作者： Shahvari, Yaser Sharbaf, Mohammadreza Rahimi, Shekoufeh Kolahdouz Tehrani, Sobhan Yassipour MDSE Research Group Department of Software Engineering University of Isfahan Isfahan Iran School of Arts University of Roehampton London United Kingdom Computer Science Department Faculty of Engineering University College London London United Kingdom

Model-driven engineering (MDE) copes with the complexity of software development by using the principles of separation of concerns and automatic transformation. In MDE, stakeholders from diverse domains collaborate concurrently on different models to quickly analyze, design, and generate complex software-intensive systems. To adopt an agile development approach in MDE, an inconsistency tolerance framework is needed. This framework postpones the resolution phase for incompatibilities caused by online cooperation, allowing temporary incompatibilities to be automatically fixed while requiring resolution only for the remaining conflicts at the appropriate time. In this paper, we propose a general framework for identifying and diagnosing inconsistencies, making decisions regarding inconsistency tolerance or intervention for resolution, and outlining tolerance strategies in agile systems modeled as multi-views. Our framework comprises three primary phases: detection, analysis, and tolerance. It is designed to identify inconsistencies in multi-view models, which are inherently more complex than single-view models. The tolerance phase employs strategies to tolerate inconsistencies, enhancing the flexibility of the agile development approach for collaborative multi-view modeling. © 2024 Copyright for this paper by its authors.

关键词： software design

来源：评论

学校读者我要写书评

暂无评论

Neural Operator-Based Framework for Time Efficient Denoising of Displacement Fields in Ultrasound Elastography

Neural Operator-Based Framework for Time Efficient Denoising...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Yihong Zhu Bo Peng School of Computer Science and Software Engineering Southwest Petroleum University Chengdu China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In ultrasound elastography, the noise present in the measured displacement fields has been a critical factor affecting the quality of the strain or elastic distribution reconstruction. Existing partial differential equation (PDE) based denoising algorithms can effectively remove noise to some extent but are limited by slow solving speeds. To address this challenge, we introduce a neural operator-based framework for denoising measured displacement fields in ultrasound elastography. By utilizing neural operators to learn a general solution operator for the denoising equation, the framework aims to achieve fast and accurate denoising of displacement fields. Experiments conducted on simulated data and tissue-mimicking phantom data demonstrate that this method performs comparably to traditional methods across various metrics. In particular, the proposed approach achieves processing speeds approximately 30 to 577 times faster than the finite difference method (FDM) in various data dimensions, making it a highly effective solution for rapid and scalable data processing in ultrasound elastography.

关键词： Ultrasonic imaging Ultrasonic variables measurement Partial differential equations Noise reduction Noise Phantoms Displacement measurement Strain measurement Elastography Strain

来源：评论

学校读者我要写书评

暂无评论

Cooperation Among Multiple Medical Institutions on Retinal Disease Identification Based on Federated Learning and Unlearning

Cooperation Among Multiple Medical Institutions on Retinal D...

引用

Communications Technology and computer science (ACCTCS), Asia-Pacific Conference on

作者： Peng Deng Wenjun Jia Nan Yang Liwen Xiao School of Computer Science and Software Engineering Southwest Petroleum University Nanchong China

ISBN: (数字)9798350359985

ISBN: (纸本)9798350359992

Due to the aging of the world's population, the incidence of retinal diseases is on the rise. Machine learning is expected to have a crucial role in identifying retinal disease. Multiple medical institutions cooperate on retinal disease identification using machine learning, which can effectively break down data silos, but it is often accompanied by privacy concerns. Federated learning (FL) solves the privacy problem in the process of collaborative model training, where data of each medical institution does not leave the local to avoid data leakage. With a series of privacy laws coming into place, it is critical to give medical institutions participating in FL training the right to be forgotten. Federated unlearning (FU) allows a medical institution requesting to exit the cooperation to eliminate its contribution from the global model. We simulate the scenarios of cooperation among multiple medical institutions on retinal disease identification, using data augmentation to deal with an imbalanced dataset and backdoor attacks to evaluate the effects of FU. Experimental results show that FL and FU have potential for application in cooperation among multiple medical institutions on retinal disease identification.

关键词： Training Privacy Data privacy Federated learning Computational modeling Sociology Retina

来源：评论

学校读者我要写书评

暂无评论

Invisible Fingerprint-based Copyright Protection for Generated Medical Image

Invisible Fingerprint-based Copyright Protection for Generat...

引用

Control, Electronics and computer Technology (ICCECT), IEEE International Conference on

作者： Nan Yang Wenjun Jia Peng Deng Wen Yang School of Computer Science and Software Engineering Southwest Petroleum University Nanchong China

ISBN: (数字)9798350380958

ISBN: (纸本)9798350380965

In the recent years, the development of generative models has stimulated the health care progress, specially medical image generation. The synthetic medical images can be applied to several fields and have many utilization, such as data aug- mentation for model training. However, this might lead to the significant copyright issues. Firstly, the data for model training may be the privacy data belong to individuals. Secondly, owners of well-trained model may have burden computation cost. Both of these two stakeholders are not will to share model to others without any compensation or the risk of the image abuse. In order to protect the copyright of the generative model for medical image and reduce the risk of the image abuse, we propose Copyright Protection mechanism for Generated Medical Image (CP4GMI) by inducing invisible fingerprint. In other words, the generated medical image should have the unique fingerprint belong to the corresponding generative model, which can be examined when image abuse happens. Numerous experiments conducted on different medical image datasets shows that our approach can track the generative model in a high-accuracy level when the fingerprint in the generated image is mostly invisible. From the perspective of users and model inventors, we propose a copyright protection mechanism that introduces invisible fingerprints, which can be printed on the generated medical images, and can be used to protect the rights when the copyright is threatened.

关键词： Training Image synthesis Computational modeling Medical services Fingerprint recognition Copyright protection Data models

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：