检索结果-内蒙古大学图书馆

IEEE Transactions on Artificial Intelligence 2025年第2期6卷 254-267页

作者： Akbar, Muhammad Azeem Khan, Arif Ali Liang, Peng Lappeenranta-Lahti University of Technology Software Engineering Department Lappeenranta15210 Finland University of Oulu M3S Empirical Software Engineering Research Unit Oulu90014 Finland Wuhan University School of Computer Science Wuhan430072 China

ChatGPT can improve software engineering (SE) research practices by offering efficient, accessible information analysis, and synthesis based on natural language interactions. However, ChatGPT could bring ethical challenges, encompassing plagiarism, privacy, data security, and the risk of generating biased or potentially detrimental data. This research aims to fill the given gap by elaborating on the key elements: motivators, demotivators, and ethical principles of using ChatGPT in SE research. To achieve this objective, we conducted a literature survey, identified the mentioned elements, and presented their relationships by developing a taxonomy. Furthermore, the identified literature-based elements (motivators, demotivators, and ethical principles) were empirically evaluated by conducting a comprehensive questionnaire-based survey involving SE researchers. In addition, we employed an interpretive structure modeling approach to analyze the relationships between the ethical principles of using ChatGPT in SE research and develop a level-based decision model. We further conducted a cross-impact matrix multiplication applied to classification analysis to create a cluster-based decision model. These models aim to help SE researchers devise effective strategies for ethically integrating ChatGPT into SE research by following the identified principles by adopting the motivators and addressing the demotivators. The findings of this study will establish a benchmark for incorporating ChatGPT services in SE research with an emphasis on ethical considerations. © 2020 IEEE.

关键词： software engineering

来源：评论

学校读者我要写书评

暂无评论

An Evolvable Knowledge Graph Supporting a Hybrid Intelligence Autonomous Driving System

An Evolvable Knowledge Graph Supporting a Hybrid Intelligenc...

引用

IEEE International Conference on software Architecture Workshops (ICSAW)

作者： Anna Teern Nada Elgendy Pertti Seppänen Tero Päivärinta Empirical Software Engineering in Software Systems and Services University of Oulu Oulu Finland

ISBN: (数字)9798331533366

ISBN: (纸本)9798331533373

Route planning is influenced by various factors, with speed and distance often prioritized in map services. However, dynamic conditions such as road maintenance, weather, or accidents can significantly impact these routes. By leveraging hybrid intelligence (HI), the collaboration between human intuition and machine efficiency, we focus on supporting decision-making in autonomous and semi-autonomous driving contexts. This study explores integrating knowledge of the dynamic conditions into HI Autonomous Driving Systems (HI-ADS) within the 6G Visible Project. The findings demonstrate the potential of knowledge graphs (KGs) to enhance decision-making by integrating evolving data and ensuring adaptability to real-world driving conditions. Based on the design objectives of learning for evolvable KGs, concrete requirements for the HI-ADS KG are established.

关键词： Technological innovation software architecture Smart cities Roads Decision making Knowledge graphs Ontologies Planning Autonomous vehicles Open data

来源：评论

学校读者我要写书评

暂无评论

Vehicle-to-Everything Services in 3GPP 5G Networks: An empirical Analysis 8

Vehicle-to-Everything Services in 3GPP 5G Networks: An Empir...

引用

8th International Workshop on Edge Systems, Analytics and Networking, EdgeSys 2025, in conjunction with ACM EuroSys 2025

作者： Basnayake, Vishaka Rathnayake, Prabhash Peltonen, Ella Empirical Software Engineering in Software Systems and Services Research University of Oulu Oulu Finland

ISBN: (纸本)9798400715594

5/6G vehicle-to-everything (V2X), particularly vehicle-to-network (V2N) communication, should provide real-time data transfer between vehicles and the cloud. In this paper, we evaluate the capability of direct commercial 5G V2N connectivity to support 5G V2X services. We perform real-world local and cross-country drive tests to measure key performance metrics such as throughput and end-to-end latency. We use the measurements to determine whether the 5G V2N meets the 3GPP standard requirements for V2X services. We then assess the feasibility of V2X services based on compliance with related latency, and throughput thresholds. Finally, we derive the signal strength thresholds required to support V2X services with low-level and high-level automation, providing insights for future 6G development. © 2025 Copyright is held by the owner/author(s).

关键词： 3GPP 5G latency RSRP signal strength throughput V2N V2X

来源：评论

学校读者我要写书评

暂无评论

An Intelligent Mobile Application to Classify Employee Mental Workload Based on Eeg Dataset Using Machine Learning

SSRN

引用

SSRN 2025年

作者： Gamage, Sameera Madushanka Keikhosrokiani, Pantea Empirical Software Engineering in Software Systems and Services University of Oulu Oulu90014 Finland Malaysia

Mental workload assessment is critical in professional environments where cognitive demands influence productivity and well-being. Traditional methods for assessing mental workload, which often rely on subjective measures, lack the reliability required for real-time applications. This study presents an innovative approach to measuring mental workload by integrating machine learning with electroencephalography data to enhance objectivity. Using an Emotiv electroencephalography headset, brain activity data was collected while participants performed job simulation tasks. The study employed a dual-model framework: a ResNet-34 deep learning model analyzed Power Spectral Density images of brain activity, achieving a classification accuracy of 70\%, while a Support Vector Machine model trained on task performance metrics and NASA Task Load Index self-assessment data independently classified mental workload levels. The outputs of these models were combined in a meta-learning framework, which further improved classification reliability and achieved significant accuracy gains. This validated meta-learning model was incorporated into a mobile application, enabling real-time assessment of mental workload. This machine learning and electroencephalography framework demonstrates potential for scalable monitoring of mental workload and adaptive task management across various industries. Future research aims to incorporate additional physiological metrics and explore clinical applications. © 2025, The Authors. All rights reserved.

关键词： Clinical research

来源：评论

学校读者我要写书评

暂无评论

CODEMINE: Building a software Development Data Analytics Platform at Microsoft

引用

IEEE software 2013年第4期30卷 64-71页

作者： Czerwonka, Jacek Nagappan, Nachiappan Schulte, Wolfram Murphy, Brendan Microsoft the Empirical Software Engineering group Microsoft the Tools for Software Engineers group

The scale and speed of today's software development efforts impose unprecedented constraints on the pace and quality of decisions made during planning, implementation, and postrelease maintenance and support for software. Decisions during the planning process include level of staffing and choosing a development model given the scope of a project and timelines. Tracking progress, course correcting, and identifying and mitigating risks are key in the development phase, as are monitoring aspects of and improving overall customer satisfaction in the maintenance and support phase. Availability of relevant data can greatly increase both the speed and likelihood of making a decision that leads to a successful software system. This article outlines the process Microsoft has gone through developing CODEMINE--a software development data analytics platform for collecting and analyzing engineering process data—its constraints, and pivotal organizational and technical choices. [ABSTRACT FROM PUBLISHER]

关键词： Analytical models and software analytics code quality Computer architecture Computer bugs Data analysis Data models metrics mining reliability software architecture software development software quality software repositories

来源：评论

学校读者我要写书评

暂无评论

Improving software Quality Prediction by Noise Filtering Techniques

引用

Journal of Computer Science & Technology 2007年第3期22卷 387-396页

作者： Taghi M.Khoshgoftaar Pierre Rebours Empirical Software Engineering Laboratory Department of Computer Science and Engineering Florida Atlantic University Boca RatonFLU.S.A.

Accuracy of machine learners is affected by quality of the data the learners are induced on. In this paper, quality of the training dataset is improved by removing instances detected as noisy by the Partitioning Filter. The fit dataset is first split into subsets, and different base learners are induced on each of these splits. The predictions are combined in such a way that an instance is identified as noisy if it is misclassified by a certain number of base learners. Two versions of the Partitioning Filter are used： Multiple-Partitioning Filter and Iterative-Partitioning Filter. The number of instances removed by the filters is tuned by the voting scheme of the filter and the number of iterations. The primary aim of this study is to compare the predictive performances of the final models built on the filtered and the un-filtered training datasets. A case study of software measurement data of a high assurance software project is performed. It is shown that predictive performances of models built on the filtered fit datasets and evaluated on a noisy test dataset are generally better than those built on the noisy （un-filtered） fit dataset. However, predictive performance based on certain aggressive filters is affected by presence of noise in the evaluation dataset.

关键词： noise filtering data quality software quality classification expected cost of misclassification voting expert

来源：评论

学校读者我要写书评

暂无评论

On building prediction systems for software engineers

引用

empirical software engineering 2000年第3期5卷 175-182页

作者： Shepperd, Martin Cartwright, Michelle Kadoda, Gada Empirical Software Engineering Research Group School of Design Engineering and Computing Bournemouth University UK

Building and evaluating prediction systems is an important activity for software engineering researchers. Increasing numbers of techniques and datasets are now being made available. Unfortunately systematic comparison is hindered by the use of different accuracy indicators and evaluation processes. We argue that these indicators are statistics that describes properties of the estimation errors or residuals and that the sensible choice of indicator is largely governed by the goals of the estimator. For this reason it may be helpful for researchers to provide a range of indicators. We also argue that it is useful to formally test for significant differences between competing prediction systems and note that where only a few cases are available this can be problematic, in other words the research instrument may have insufficient power. We demonstrate that this is the case for a well known empirical study of cost models. Simulation, however, could be one means of overcoming this difficulty.

关键词： Computer software selection and evaluation

来源：评论

学校读者我要写书评

暂无评论

Kubernetes Edge/Cloud Continuum Task Offloading Framework for Vehicular Computing 41

Kubernetes Edge/Cloud Continuum Task Offloading Framework fo...

引用

41st Annual Doctoral Symposium of Computer Science, TKTP 2024

作者： Mahmoodi, Alireza Bakhshi Zadi Peltonen, Ella Empirical Software Engineering in Software Systems and Services University of Oulu Finland

Cars have significantly been transformed to the point of autonomously driving in complex situations by sensing their surroundings and inferring insights based on sensor inputs. Even though smart cars can process the vast majority of data coming from various in-vehicle-installed sensors such as radars, LiDAR, cameras, and so on, the amount of data processing required is ever-growing, along with the demand for more real-time services for novel driving applications. In addition, sustainability and battery-longevity perspectives appreciate the computation of the vehicle-sensor data to be offloaded to the edge-cloud continuum. This article introduces a Kubernetes-based framework that can be utilized on cloud/edge servers to facilitate various tasks and computation offloading for smart vehicles. The work is ongoing, and we present the preliminary results about the framework’s validity by employing an object recognition task on both edge and cloud computing servers to showcase the proposed architecture’s feasibility. An incidental finding regarding latency is also presented in the experiment. Moreover, we discuss development challenges related to implementing an edge-cloud continuum on vehicular computing. © 2024 Copyright for this paper by its authors.

关键词： Computation offloading

来源：评论

学校读者我要写书评

暂无评论

Kuura: Leveraging Eclipse Kuksa in Vehicular Data Collection and Digital Twin Creation Environment 41

Kuura: Leveraging Eclipse Kuksa in Vehicular Data Collection...

引用

41st Annual Doctoral Symposium of Computer Science, TKTP 2024

作者： Timonen, Olli Bomström, Toni Stafford, Nicklas Määttä, Samuli Mahmoodi, Alireza Bakhshi Zadi Päivärinta, Tero Peltonen, Ella Empirical Software Engineering in Software Systems and Services University of Oulu Finland

Increased sensing and computing capabilities in cars are crucial for advanced traffic and driving automation. However, novel data delivery, testing, and machine learning pipelines are still needed to harness the full capabilities of automotive sensing solutions. At the same time, vehicular digital twins are needed to enable versatile testing and simulation capabilities. This paper depicts the Vehicle-In-The-Loop (VIL) cloud interface and verifies data consistency regardless of the source. The study aims to determine how data collected from simulation corresponds to real test drive data. The data is collected from both simulation and actual test drives. Utilising the MQTT protocol, data is stored on a cloud server and further fed into Unreal Engine 5, where the test drive is replayed, and its correspondence to the real drive is ensured. This work offers a new perspective on verifying data consistency between simulated and real test drives and complements the vehicle abstraction opportunities provided by Eclipse KUKSA. Our results highlight digital twin creation as a part of automotive software development and set premises for testing and validating complex use cases, such as traffic accidents and extreme weather, that can rarely or only with severe expenses be tested in real-life situations. © 2022 Copyright for this paper by its authors.

关键词： Automobile testing

来源：评论

学校读者我要写书评

暂无评论

An empirical evaluation of architectural alternatives for J2EE and Web services

An empirical evaluation of architectural alternatives for J2...

引用

11th Asia-Pacific software engineering Conference (APSEC 2004)

作者： Liu, Y Gorton, I Empirical Software Engineering Group National ICT Australia (NICTA) Australia

ISBN: (纸本)0769522459

Component-based technologies such as J2EE and NET have been widely adopted to develop web-based applications. With the emerging use of Web Services and service-based architectures, such component technologies provide the necessary backend components to execute application business logic, supporting a variety of architectural alternatives. The combination of component technologies with Web Services further extends the set of architecture choices, with each providing different levels of performance. It is therefore important for an architect to understand these performance implications in the early stages of the architecture design. In this paper, we measure and analyze the performance of four alternative architectures using J2EE and Web Services. The purpose is to investigate the effects on performance, identify both hardware and software bottlenecks, and analyze the architectural design trade-offs.

关键词： World Wide Web

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：