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检索条件"任意字段=International Conference on Multimedia, Software Engineering and Computing"

共 66504 条记录，以下是281-290 订阅

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An Enhanced Role of AI in SDLC in Implementing Maintenance System 4

An Enhanced Role of AI in SDLC in Implementing Maintenance S...

引用

4th IEEE international conference on Advance computing and Innovative Technologies in engineering, ICACITE 2024

作者： Kumar, Yogendra Babu, Ch.Raja Kishore Deorari, Maha Medha Jasim, Laith Sharma, Komal Vinothkumar, K. Gla University Department of Electrical Engineering Mathura India Cmr College of Engineering & Technology Department of Cse India Uttaranchal Institute of Pharmaceutical Sciences Uttaranchal University Dehradun India The Islamic University Najaf Iraq Chitkara University Chitkara Centre for Research and Development Himachal Pradesh India Psna College of Engineering and Technology India

ISBN: (纸本)9798350360165

In the hastily evolving domain of software development, Artificial Intelligence (AI) is a pivotal pressure reworking software checking out from its traditional, exertions-intensive methodologies to greater sophisticated, computerized techniques. This research paper delves into the multifaceted effect of AI on software checking out, elucidating the way it now not only augments the performance and precision of testing however also appreciably reduces the time to market for software products. By integrating AI technology which include gadget mastering, deep studying, and herbal language processing, this study illustrates the ability of AI to revolutionize best assurance practices with the aid of facilitating smarter automated trying out techniques, improving check case generation, and improving fault detection mechanisms. Furthermore, the studies highlight AI's instrumental function in addressing the complexities of modern software programs, in the end leading to the development of strong, blunders-loose software program solutions. The findings underscore the transformative capability of AI in software program trying out, heralding a brand-new generation of innovation and performance inside the software program development lifecycle. © 2024 IEEE.

关键词： software design

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UCTB: An Urban computing Tool Box for Building Spatiotemporal Prediction Services 2

UCTB: An Urban Computing Tool Box for Building Spatiotempora...

引用

2nd IEEE international conference on software Services engineering (SSE)

作者： Fang, Jiangyi Chen, Liyue Chai, Di Hong, Yayao Xie, Xiuhuai Chen, Longbiao Wang, Leye Minist Educ Key Lab High Confidence Software Technol Beijing Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China Fujian Key Lab Sensing & Comp Smart Cities Xiamen Peoples R China Xiamen Univ Sch Informat Xiamen Peoples R China Peking Univ Sch Comp Sci Beijing Peoples R China

ISBN: (纸本)9798350368529;9798350368512

Spatiotemporal prediction (STP) service is one of the key infrastructure applications in smart cities. Currently, most of the existing STP services are constructed following the workflow of building deep learning (DL) applications while neglecting the importance of domain knowledge and region partition. However, the performance and interpretability of STP are highly related to them. As a result, there is an urgent requirement to develop a thorough and tailored workflow for STP services. To address this gap, we propose a novel workflow including two factors above as intermediate procedures. Based on the workflow, we design and implement an STP toolbox called UCTB (Urban computing Tool Box) assisting practitioners in the rapid construction of STP services, which can manage multiple spatiotemporal domain knowledge, support various region partition algorithms, and possess state-of-the-art models simultaneously. The relevant code and supporting documents have been open-sourced at https://***/uctb/UCTB.

关键词： spatiotemporal prediction toolbox software service workflow

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Cotton Leaf Disease Classification and Pesticide Recommendation 2

Cotton Leaf Disease Classification and Pesticide Recommendat...

引用

2nd IEEE international conference on Networks, multimedia and Information Technology, NMITCON 2024

作者： Jamadar, Bhagesh Harikant, Bhavana Manjunath Chaithra, B.J. Mohit, J.S. Vasudevan, Vani Nitte Meenakshi Institute of Technology Computer Science Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350372892

The decline in cotton production due to various diseases poses significant challenges to agriculture. In response, this paper presents the development of a software application titled 'Cotton Leaf Disease Classification and Pesticide Recommendation.' The application aims to classify diseases affecting cotton leaves, recommend suitable pesticides, and suggest nearby shops for pesticide procurement. Focusing primarily on prevalent cotton leaf diseases such as cercospora, bacterial blight, curl virus, fusarium, and Ascochyta, the software employs image preprocessing techniques to process cotton leaf images. Convolutional neural network technologies are utilized for training the datasets to accurately classify diseases. Upon classification, the application suggests appropriate pesticides and nearby vendors, and sends the results to the user's email address. This software endeavors to mitigate the impact of cotton leaf diseases on agricultural productivity, particularly in regions like Karnataka, India. © 2024 IEEE.

关键词： Application programs

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Security Risk Assessment on Cloud: A Systematic Mapping Study 24

Security Risk Assessment on Cloud: A Systematic Mapping Stud...

引用

28th international conference on Evaluation and Assessment in software engineering (EASE)

作者： Annunziata, Giusy Sheykina, Alexandra Palomba, Fabio De Lucia, Andrea Catolino, Gemma Ferrucci, Filomena Univ Salerno Salerno Italy

ISBN: (纸本)9798400717017

Cloud computing has become integral to modern organizational operations, offering efficiency and agility. However, security challenges such as data loss and downtime necessitate tailored compliance solutions. Risk assessment is crucial for identifying and mitigating cloud-related threats, yet a standardized approach remains elusive. Our study aims to fill this gap by conducting a systematic mapping study on the prevailing methodologies. Through a meticulous analysis of 21 scholarly papers, we explore various aspects of security risk assessment for the cloud. The results provide valuable insights into delivery models, standards, and validation practices, contributing to a comprehensive understanding of cloud risk assessment.

关键词： Risk assessment security testing cloud computing

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Leveraging Machine Learning for Enhanced software Defect Prediction and Quality Assurance: A Comparative Analysis

Leveraging Machine Learning for Enhanced Software Defect Pre...

引用

2024 international conference on computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Mohandas, Parthiban Duraipandian, Amaravathi Chaitanya, Voleti Tharun, Allam Naga Vishnuvardhan, Divi Bala Kumar, Donelli Naga Vinay Computer Science and Engineering Department Sasi Institute of Technology and Engineeering Tadepalligudem India

ISBN: (纸本)9798331510299

The evolution of software engineering practices has heightened the emphasis on quality assurance, particularly in defect prediction and testing methodologies. Traditional approaches often fall short in addressing the complexities of modern software systems, prompting the integration of advanced technologies such as deep learning and machine learning. Recent research highlights the development of robust models for predicting defect density, which effectively tackle issues of data sparsity, alongside the introduction of specialized validation frameworks for AI-driven software that utilize innovative testing methods. Additionally, advancements in automated testing practices, including the use of large language models for test case generation and ensemble machine learning for bug classification, underscore the critical role of efficient maintenance in ensuring software quality. Furthermore, novel frameworks for validating unsupervised learning systems bridge user expectations with system performance evaluations. Collectively, these contributions reveal a trend towards leveraging cutting-edge methodologies to address the multifaceted challenges of software quality, emphasizing the need for adaptive and innovative strategies as software systems continue to evolve. © 2024 IEEE.

关键词： software quality

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Are Unikernels Ready for Serverless on the Edge? 12

Are Unikernels Ready for Serverless on the Edge?

引用

12th international conference on Cloud engineering

作者： Moebius, Felix Pfandzelter, Tobias Bermbach, David Tech Univ Berlin Berlin Germany Einstein Ctr Digital Future Scalable Software Syst Res Grp Berlin Germany

ISBN: (纸本)9798331528690;9798331528706

Function-as-a-Service (FaaS) is a promising edge computing execution model but requires secure sandboxing mechanisms to isolate workloads from multiple tenants on constrained infrastructure. Although Docker containers are lightweight and popular in open-source FaaS platforms, they are generally considered insufficient for executing untrusted code and providing sandbox isolation. Commercial cloud FaaS platforms thus rely on Linux microVMs or hardened container runtimes, which are secure but come with a higher resource footprint. Unikernels combine application code and limited operating system primitives into a single purpose appliance, reducing the footprint of an application and its sandbox while providing full Linux compatibility. In this paper, we study the suitability of unikernels as an edge FaaS execution environment using the Nanos and OSv unikernel tool chains. We compare performance along several metrics such as cold start overhead and idle footprint against sandboxes such as Firecracker Linux microVMs, Docker containers, and secure gVisor containers. We find that unikernels exhibit desirable cold start performance, yet lag behind Linux microVMs in stability. Nevertheless, we show that unikernels are a promising candidate for further research on Linux-compatible FaaS isolation.

关键词： serverless edge computing unikernels

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A Metric for Measuring software engineering Post-Graduate Outcomes 23

A Metric for Measuring Software Engineering Post-Graduate Ou...

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IEEE/ACM 45th international conference on software engineering - software engineering Education and Training (ICSE-SEET)

作者： Breaux, Travis D. Moritz, Jennifer Carnegie Mellon Univ Software & Societal Syst Dept Pittsburgh PA 15213 USA Carnegie Mellon Univ Univ Adv Pittsburgh PA USA

ISBN: (纸本)9798350322590

Professional software engineering (SE) degree programs provide students with the education and skills needed to enter a new SE career, or take on increasing responsibility within their current career. An important metric for evaluating such programs is the impact that completing the program has on postgraduate, career outcomes. Apart from hiring rates and median salaries, this is challenging to measure, because alumni survey response rates are frequently low, and without alumni feedback, insight into individual career advancement after graduation is difficult to observe. In this paper, we propose a new metric, called Career Velocity, that measures the impact of a degree program on alumni promotion into senior positions. The metric requires tracing alumni directory information, consisting of a person's full name, degree name, and graduation year, to public data that includes employment histories, before computing the number of months prior to promotion into a senior SE position. The metric was developed and evaluated on a mix of six degree programs, including undergraduate and graduate computer science, software engineering and data science programs. The metric was further evaluated by assessing the impact of a graduate's number of months of industry experience prior to graduation. The results suggest that, independent of prior industry experience, specialized education that targets advancement in a specific career class, e.g., software engineering, leads to faster career progression than general education.

关键词： software engineering education career outcomes

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software Fault Prediction in Service-Oriented Based Systems 5

Software Fault Prediction in Service-Oriented Based Systems

引用

5th IEEE international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Singh, Raghuraj Kumar, Kuldeep Dr. B. R. Ambedkar National Institute of Technology Department of Computer Science & Engineering Punjab Jalandhar India National Institute of Technology Department of Computer Engineering Haryana Kurukshetra India

ISBN: (纸本)9798350383522

software fault prediction plays a crucial role in enhancing software quality and minimizing maintenance cost. Primary goal of software fault prediction is to identify flaws before testing stage, that will save the expense and duration of software development while improving the software product's quality. In recent years, deep learning techniques have gained significant attention in the field of software fault prediction due to their ability to extract complex patterns and features from large-scale software datasets. This paper aims to provide a comprehensive overview of the existing research on software fault prediction using deep learning techniques. It covers a wide range of studies, including various deep learning models, feature engineering approaches, evaluation metrics, and datasets used in this domain. We examine strategies for predicting software faults in Service-Oriented Architecture based systems using deep learning and hybrid optimization-enabled deep learning methods. The findings reveal that hybrid deep learning and nature-inspired enabled deep learning techniques are more effective than just using machine learning and deep learning approaches. © 2024 IEEE.

关键词： Deep Learning Machine Learning Service Oriented Architecture (SOA) software Fault Prediction (SFP)

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gHermes: Application-Unaware Acceleration for Cloud Rendering and computing with Efficient GPU Utilization 36

gHermes: Application-Unaware Acceleration for Cloud Renderin...

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36th international conference on software engineering and Knowledge engineering, SEKE 2024

作者： Hu, Xuyan Gu, Yicheng Qi, Zhengwei Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)1891706594

GPUs, as crucial tools for intensive computing tasks, are primarily used for rendering and computation. However, due to the high cost of GPUs and their frequent updates, owning powerful local GPUs is considered a luxury. Offloading tasks to the cloud is an effective solution. This method allows users to leverage the powerful capabilities of cloud-based high-performance computing resources to perform complex rendering tasks more quickly and cost-effectively. However, there has not yet been a solution that simultaneously considers both rendering and computation without requiring any modifications to local applications. This paper introduces gHermes, an application-unaware acceleration solution that automatically generates code to intercept rendering and computing tasks, overcoming the limitations of existing technologies. Additionally, for computational tasks, gHermes also enables fine-grained control of GPU utilization. Experimental results show that gHermes can handle both computing and rendering tasks efficiently. While ensuring the Quality of Service (QoS) requirements for rendering tasks (30-60 FPS), it effectively utilizes GPU resources for computation services. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Rendering (computer graphics)

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Bringing Industry-Grade Code Quality and Practices into software engineering Education (Doctoral Consortium) 24

Bringing Industry-Grade Code Quality and Practices into Soft...

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24th Koli Calling

作者： Birillo, Anastasiia JetBrains Res Belgrade Serbia Univ Utrecht Utrecht Netherlands

ISBN: (纸本)9798400710384

Using professional development tools and practices is an essential part of being a programmer. However, beginners often struggle with professional tools. In this work, we ask the question: "How can we adapt professional programming tools to improve software engineering education?" and aim to find efficient ways to solve this problem.

关键词： Code Quality Assessment Code Formatting LLMs Generative AI Next-Step Hints

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