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

共 65256 条记录，以下是151-160 订阅

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What Makes Team[s] Work? A Study of Team Characteristics in software engineering Projects 22

What Makes Team[s] Work? A Study of Team Characteristics in ...

引用

18th Annual ACM conference on international computing Education Research (ICER)

作者： Presler-Marshall, Kai Heckman, Sarah Stolee, Kathryn T. North Carolina State Univ Raleigh NC USA

ISBN: (纸本)9781450391948

Teaming is a core component in practically all professional software engineering careers, and as such, is a key skill taught in many undergraduate Computer Science programs. However, not all teams manage to work together effectively, and in education, this can deprive some students of successful teaming experiences. In this work, we seek to gain insights into the characteristics of successful and unsuccessful undergraduate student teams in a software engineering course. We conduct semi-structured interviews with 18 students who have recently completed a team-based software engineering course to understand how they worked together, what challenges they faced, and how they tried to overcome these challenges. Our results show that common problems include communicating, setting and holding to deadlines, and effectively identifying tasks and their relative difficulty. Additionally, we find that self-reflection on what is working and not working or external motivators such as grades help some, but not all, teams overcome these challenges. Finally, we conclude with recommendations for educators on successful behaviours to steer teams towards, and recommendations for researchers on future work to better understand challenges that teams face.

关键词： software engineering teams team challenges team dysfunction team characteristics student teams

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Meta-Modelling Kindness 24

Meta-Modelling Kindness

引用

27th international conference on Model Driven engineering Languages and Systems (MODELS)

作者： Alrimawi, Faeq Nuseibeh, Bashar Univ Limerick Lero SFI Res Ctr Software Limerick Ireland Open Univ Sch Comp & Commun Milton Keynes Bucks England

ISBN: (纸本)9798400705045

Kindness is a psycho-social phenomenon that is also recognized as an important pro-social behaviour. The use of digital technology provides opportunities to promote kindness in various ways, such as in social media campaigns and online communities. In principle, software engineers are well positioned to develop automated systems that can facilitate software-mediated kindness. However, in practice, incorporating kindness concerns explicitly in the development and use of software systems is challenging: kindness is highly context dependent, affected by a range of factors such as intentions and opportunity. In this paper, we explore systematic ways in which kindness concerns can be considered by software engineers. We propose a novel meta-model that captures essential entities and relations associated with kindness. The meta-model enables the representation of possible instances or opportunities for performing acts of kindness, by considering the actors involved (such as giver, receiver, and observer), their psychological and social attributes that promote kindness (such as emotional states and social relatedness), the acts needed to fulfil kindness opportunities (such as motivation, ability, and timeliness), and other contextual factors (such as location and time). Our meta-model is demonstrated through two software application scenarios that enable charitable donations and kindness in business. Overall, our proposal offers a first, tentative, but concrete step towards enabling kind computing, and promoting kindness in software systems.

关键词： Kindness Meta-Modelling software engineering Kind computing

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software Bug Prediction using Machine Learning 15

Software Bug Prediction using Machine Learning

引用

15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Sathiyaraj, A. Angel, T.G. Ruby Turukmane, Anil V. Ramkumar, D. Guravaiah, Y. Department of Information Technology School of Computing Sathyabama Institute of Science and Technology Tamil Nadu Chennai600 119 India School of Computer Science and Engineering VIT-AP University Inavolu Andhra Pradesh Amaravati522237 India Department of Computer Science and Engineering MJR College of Engineering and Technology Diguvapokulavaripalli Pulicherla Andra Pradesh Chittoor District517214 India

ISBN: (纸本)9798331300579

The entire success of software is impacted by software bug prediction (SBP), a crucial part of the software development and maintenance life cycles. It is necessary to anticipate issues in order to increase the software's dependability, efficiency, and cost. In spite of the fact that a number of methods have been put out in the literature, it is difficult to create a reliable bug prediction model. In this paper, a machine learning (ML) based prediction method for software issues is introduced. Algorithms for guided machine learning that are based on historical data have been used to predict software errors. Two of the classifiers are Support Vector Machine and Naive Bayes (NB). How accurate and useful ML methods can be employed was demonstrated through the evaluation process. Included is the utilization of a comparative measure. © Grenze Scientific Society, 2024.

关键词： Prediction models

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Feature Selection using Genetic Algorithm for software Fault Prediction 3

Feature Selection using Genetic Algorithm for Software Fault...

引用

3rd international conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Pethe, Yoginee Surendra Das, Himansu Kiit Deemed to Be University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350375190

The field of software engineering has seen an increase in research aimed at predicting software defects. The importance of non-functional defects in this study can effectively predict software defects, thus simplifying software testing, reducing costs, and improving the overall software testing process. However, the presence of noise in the data set often causes software bug prediction performance to decrease. In this work, a Genetic Algorithm (GA) is used to improve the accuracy of software fault prediction. It is used as a feature selection (FS) algorithm. The technology was evaluated using NASA's Metrics Data Repository dataset. The results show that the prediction performance of most products is greatly improved when the proposed method is used. © 2024 IEEE.

关键词： Genetic algorithms

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NNVISR: Bring Neural Network Video Interpolation and Super Resolution into Video Processing Framework 24

NNVISR: Bring Neural Network Video Interpolation and Super R...

引用

32nd ACM international conference on multimedia, MM 2024

作者： Tong, Yuan Hu, Mengshun Wang, Zheng National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan University Wuhan China

ISBN: (纸本)9798400706868

We present NNVISR - an open-source filter plugin for the VapourSynth video processing framework, which facilitates the application of neural networks for various kinds of video enhancing tasks, including denoising, super resolution, interpolation, and spatio-temporal super-resolution. NNVISR fills the gap between video enhancement neural networks and video processing pipelines, by accepting any network that enhances a group of frames, and handling all other network agnostic details during video processing. NNVISR is publicly released at https://***/tongyuantongyu/vs-NNVISR. © 2024 ACM.

关键词： Open systems

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Evaluating the Performance of a D -Wave Quantum Annealing System for Feature Subset Selection in software Defect Prediction 5

Evaluating the Performance of a D -Wave Quantum Annealing Sy...

引用

2024 international conference on Quantum computing and engineering

作者： Mandal, Ashis Kumar Nadim, Md Roy, Chanchal K. Roy, Banani Schneider, Kevin A. Univ Saskatchewan Dept Comp Sci Saskatoon SK Canada

ISBN: (纸本)9798331541378

Predicting software defects early in the development process not only enhances the quality and reliability of the software but also decreases the cost of development. A wide range of machine learning techniques can be employed to create software defect prediction models, but the effectiveness and accuracy of these models are often influenced by the choice of appropriate feature subset. Since finding the optimal feature subset is computationally intensive, heuristic and metaheuristic approaches are commonly employed to identify near-optimal solutions within a reasonable time frame. Recently, the quantum computing paradigm quantum annealing (QA) has been deployed to find solutions to complex optimization problems. This opens up the possibility of addressing the feature subset selection problem with a QA machine. Although several strategies have been proposed for feature subset selection using a QA machine, little exploration has been done regarding the viability of a QA machine for feature subset selection in software defect prediction. This study investigates the potential of 11 -Wave QA system for this task, where we formulate a mutual information (NM -based filter approach as an optimization problem and utilize a 1) -Wave Quantum Processing Unit (QPU) solver as a QA solver for feature subset selection. We evaluate the performance of this approach using multiple software defect datasets from the AEEM, JIRA, and NASA projects. We also utilize a 1) -Wave classical solver for comparative analysis. Our experimental results demonstrate that QA -based feature subset selection can enhance software defect prediction. Although the 1) -Wave QPU solver exhibits competitive prediction performance with the classical solver in software defect prediction, it significantly reduces the time required to identify the best feature subset compared to its classical counterpart.

关键词： Terms combinatorial optimization problem feature subset selection quantum computing quantum annealing software defect prediction

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Speedcode: software Performance engineering Education via the Coding of Didactic Exercises

Speedcode: Software Performance Engineering Education via th...

引用

1st international conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Kaler, Tim Chen, Xuhao Wheatman, Brian Curtis, Dorothy Hoppe, Bruce Schardl, Tao B. Leiserson, Charles E. MIT CSAIL 77 Massachusetts Ave Cambridge MA 02139 USA Johns Hopkins Univ Baltimore MD USA Connect Associates Boston MA USA

ISBN: (纸本)9798350364613;9798350364606

This paper introduces Speedcode, an online programming platform that aims to improve the accessibility of software performance-engineering education. At its core, Speedcode provides a platform that lets users gain hands-on experience in software performance engineering and parallel programming by completing short programming exercises. Speedcode challenges users to develop fast multicore solutions for short programming problems and evaluates their code's performance and scalability in a quiesced cloud environment. Speedcode supports parallel programming using OpenCilk, task-parallel computing platform that is open-source and easy to program, teach and use for research. Speedcode aims to reduce barriers to learning and teaching software performance engineering. It allows users to run and evaluate their code on modern multicore machines from their own computer without installing any software. This provides users an easy introduction to the topic, and enables teachers to more easily incorporate lessons on software performance engineering into their courses without incurring the onerous overhead of needing to setup computing environments for their students.

关键词： education online learning platforms parallel programming software performance engineering

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Enhancement of Circuit Theory Course Using Low-Cost software-Defined Instruments 11

Enhancement of Circuit Theory Course Using Low-Cost Software...

引用

11th international conference on Electrical, Electronic and computing engineering, IcETRAN 2024

作者： Basta, Nikola Grujičić, Nemanja Madžarević, Aleksa Ivaniš, Milka Potrebić School of Electrical Engineering University of Belgrade Belgrade Serbia

ISBN: (纸本)9798350386998

In this paper, we show use-case examples of the application of affordable programmable measurement hardware in circuit analysis as part of a graduate course in Electric-circuit theory. Besides basic measurement modes, some advanced software-enabled capabilities in experiment control, post-processing and data visualization are shown. In conclusion, we consider different setup realizations, as well as the advantages and limitations of such software-defined equipment. © 2024 IEEE.

关键词： MATLAB

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Simulation of multimedia AR Intelligent Tourism System with IoT and Cloud computing 3

Simulation of Multimedia AR Intelligent Tourism System with ...

引用

3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Wang, Honglan Yan, Juan College of Tourism Management Jinan Vocational College Shandong Jinan China Tourism Management School Jinan Vocational College Shandong Jinan China

ISBN: (纸本)9798350317022

With the rapid development of the Internet of Things and cloud computing technology, the application of multimedia augmented reality (AR) intelligent tourism systems in the tourism industry has gradually increased. Traditional systems have problems such as lack of interactivity and personalized service. This paper proposes a multimedia AR intelligent tourism system based on the Internet of Things and computing. The article adopts the method of system design and simulation, researches and analyzes the Internet of Things and cloud computing technologies to determine the basic components and functions required by the system, then designs the system architecture and develops the corresponding software and hardware modules, and finally uses simulation tools to simulate and evaluate the system. After system testing, the response time of the system in this article is between 0.65-1.56S. The system supports real-time AR navigation and information display, and users can interact with the system through mobile phones or AR glasses. © 2023 IEEE.

关键词： Cloud computing Intelligent Tourism Internet of Things multimedia AR

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Aegis: Attribution of Control Plane Change Impact across Layers and Components for Cloud Systems 23

Aegis: Attribution of Control Plane Change Impact across Lay...

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IEEE/ACM 45th international conference on software engineering - software engineering in Practice (ICSE-SEIP)

作者： Yan, Xiaohan Hsieh, Ken Liyanage, Yasitha Ma, Minghua Chintalapati, Murali Lin, Qingwei Dang, Yingnong Zhang, Dongmei Microsoft Azure Redmond WA 98052 USA Microsoft Res Beijing Peoples R China

ISBN: (纸本)9798350300376

Modern cloud control plane infrastructure like Microsoft Azure has evolved into a complex one to serve customer needs for diverse types of services and adequate cloud-based resources. On such interconnected system, implementing changes at one component can have an impact on other components, even across different hierarchical computing layers. As a result of the complexity and interconnected nature of the cloud-based services, it poses a challenge to correctly attribute service quality degradation to a control plane change, to infer causality between the two and to mitigate any negative impact. In this paper, we present Aegis, an end-to-end analytical service for attributing control plane change impact across computing layers and service components in large-scale real-world cloud systems. Aegis processes and correlates service health signals and control plane changes across components to construct the most probable causal relationship. Aegis at its core leverages a domain knowledge-driven correlation algorithm to attribute platform signals to changes, and a counterfactual projection model to quantify control plane change impact to customers. Aegis can mitigate the impact of bad changes by alerting service team and recommending pausing the bad ones. Since Aegis' inception in Azure Control Plane 12 months ago, it has caught several bad changes across service components and layers, and promptly paused them to guard the quality of service. Aegis achieves precision and recall around 80% on real-world control plane deployments.

关键词： Safe Deployment Regression Detection Impact Assessment Counterfactual Analysis Cloud computing

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