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A Vision of DevOps Requirements Change Management Standardization

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arXiv 2022年

DevOps (development and operations) aims to shorten the software development process and provide continuous delivery with high software quality. To get the potential gains of DevOps, the software development industry considering global software development (GSD) environment to hire skilled human resources and round-the-clock working hours. However, due to the lack of frequent communication and coordination in GSD, the planning and managing of the requirements change process becomes a challenging task. As in DevOps, requirements are not only shaped by development feedback but also by the operations team. This means requirements affect development, development affects operations and operations affect requirements. However, DevOps in GSD still faces many challenges in terms of requirement management. The purpose of this research project is to develop a DevOps requirement change management and implementation maturity model (DevOps-RCMIMM) that could assist the GSD organizations in modifying and improving their requirement management process in the DevOps process. The development of DevOps-RCMIMM will be based on the existing DevOps and RCM literature, industrial empirical study, and understanding of factors that could impact the implementation of the DevOps requirement change management process in the domain of GSD. This vision study presents the initial results of a systematic literature review that will contribute to the development of maturity levels of the proposed DevOps-RCMIMM. © 2022, CC BY.

关键词： Standardization

UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobility

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arXiv 2025年

作者： Tian, Yonglin Lin, Fei Li, Yiduo Zhang, Tengchao Zhang, Qiyao Fu, Xuan Huang, Jun Dai, Xingyuan Wang, Yutong C., Tian B., Li Y., Lv L., Kovács F.-Y., Wang The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology 999078 China School of Automation Beijing Institute of Technology Beijing100081 China School of Software Northwestern Polytechnical University Xi’an710129 China College of Mechanical and Vehicle Engineering Hunan University Changsha410082 China John von Neumann Faculty of Informatics Obuda University BudapestH-1034 Hungary

Low-altitude mobility, exemplified by unmanned aerial vehicles (UAVs), has introduced transformative advancements across various domains, like transportation, logistics, and agriculture. Leveraging flexible perspectives and rapid maneuverability, UAVs extend traditional systems’ perception and action capabilities, garnering widespread attention from academia and industry. However, current UAV operations primarily depend on human control, with only limited autonomy in simple scenarios, and lack the intelligence and adaptability needed for more complex environments and tasks. The emergence of large language models (LLMs) demonstrates remarkable problem-solving and generalization capabilities, offering a promising pathway for advancing UAV intelligence. This paper explores the integration of LLMs and UAVs, beginning with an overview of UAV systems’ fundamental components and functionalities, followed by an overview of the state-of-the-art LLM technology. Subsequently, it systematically highlights the multimodal data resources available for UAVs, which provide critical support for training and evaluation. Furthermore, key tasks and application scenarios where UAVs and LLMs converge are categorized and analyzed. Finally, a reference roadmap towards agentic UAVs is proposed to enable UAVs to achieve agentic intelligence through autonomous perception, memory, reasoning, and tool utilization. Related resources are available at https://***/Hub-Tian/UAVs Meet LLMs. Copyright © 2025, The Authors. All rights reserved.

关键词： Drones

Rem sleep stage detection of parkinson’s disease patients with rbd 23rd

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Rem sleep stage detection of parkinson’s disease patients w...

23rd International Conference on Business Information systems, BIS 2020

作者： Bisgin, Pinar Houta, Salima Burmann, Anja Lenfers, Tim Fraunhofer Institute for Software and Systems Engineering HealthCare Dortmund Germany Department of Medical Informatics Heilbronn University Heilbronn Germany Ruprecht-Karls University Heidelberg Heidelberg Germany

ISBN: (纸本)9783030533366

REM sleep behavior disorder (RBD) is commonly associated with Parkinson’s disease. In order to find adequate therapy for affected persons and to seek suitable early Parkinson Patterns, the investigation of this phenomenon is highly relevant. The analysis of sleep is currently done by manual analysis of polysomnography (PSG), which leads to divergent scoring results by different experts. Automated sleep stage detection can help deliver accurate, reproducible scoring results. In this paper, we evaluate different machine learning models from the PSG signals for automatic sleep stage detection. The highest accuracy of 87.57% was achieved by using the Random Forest algorithm. © Springer Nature Switzerland AG 2020.

关键词： Neurodegenerative diseases

A multi-model approach to construction site safety: Fault trees, Bayesian networks, and ontology reasoning

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Expert systems with Applications 2025年 288卷

作者： Shi, Donghui Gan, Shuling Zurada, Jozef Guan, Jian Wang, Feilong Weichbroth, Pawel School of Electronics and Information Engineering Anhui Jianzhu University Anhui Province Hefei230601 China Anhui Province Key Laboratory of Intelligent Building and Building Energy Saving Anhui Jianzhu University Anhui Hefei230022 China Department of Information Systems Analytics and Operations College of Business University of Louisville 2301 S 3rd St LouisvilleKY40292 United States School of Electronic Science and Engineering Hunan University of Information Technology Hunan Changsha410151 China Department of Software Engineering Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology Narutowicza 11/12 pomorskie Gdansk80-233 Poland

With the rapid expansion of the construction industry, accidents on construction sites have been increasingly common. Among these, collapses of earthwork foundation pits are particularly significant due to the sheer weight of the collapsed materials and the vast affected areas, resulting in substantial casualties and economic losses. This study aims to determine the causes of these construction safety and collapse incidents and understand their relationships to enable effective supervision and prevention during construction. Initially, the LDA model is used to categorize historical construction safety accident reports, and text mining is applied utilizing R language platform and the TF-IDF measure to extract keywords related to accident causative factors from historical accident reports. Following this, risk factors are evaluated to find the basic, intermediate, and top events of the accident, constructing a fault tree of casualties from earthwork foundation pit collapse (EFPC) accidents and analyzing the structural significance of risk factors. The fault tree is converted into a Bayesian network through image and numerical mapping, allowing the analysis of node sensitivity and the prediction of top event probability for informed construction accident prediction and prevention. Lastly, the study constructs an ontology knowledge base and knowledge graph in the realm of building safety, and establishes an ontology reasoning model using the Pellet reasoning machine and SWRL reasoning rules. The novel approach in this study involves integrating multiple advanced methodologies and provides a comprehensive framework and knowledge foundation for enhancing construction accident prediction and prevention as well as understanding and mitigating the risk-causing factors of earthwork foundation pit collapses in the construction industry. © 2025 Elsevier Ltd

关键词： Construction industry

Practitioner's Perspective on Motivators of Agile in Global software Development

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arXiv 2022年

作者： Akbar, Muhammad Azeem Al-Sanad, Abeer Rafi, Saima Wang, Yuqing Alzahrani, Musaad Department of Software Engineering LUT University Lappeenranta Finland Imam Mohammad Ibn Saud Islamic University Information Systems Department Riyadh11432 Saudi Arabia University of Murcia Department of Informatics and Systems Murcia30100 Spain M3S Univeristy of Oulu Finland Department of Computer Science Albaha University Albaha65799 Saudi Arabia

In modern software development world, experts are trying to provide the best solutions to their clients. To achieve this, the organizations opt for the agile software development process as it enables them to develop and deliver the product in-time and as per client's expectations. Consequently, in software engineering industry, the Global software Development (GSD) is the most widely considering software development paradigm as it offers significant strategic and business gains. Seeking the benefits of GSD, the European software engineering organizations are outsourcing their development activities in developing countries. Considering the criticalities of agile adoption in GSD, this work empirically studies the motivators that could positively influence the execution of agile-based GSD in European software industry. A quantitative survey was conducted and data from 139 practitioners working in agile and GSD based projects was collected. The collected observations were further analyzed using Smart-PLS (3.0). The results show that the identified motivators are important to consider by industry experts to successfully apply the agile practices in GSD context. © 2022, CC BY.

关键词： Developing countries

Code Smell Detection Using Whale Optimization Algorithm

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Computers, Materials & Continua 2021年第8期68卷 1919-1935页

作者： Moatasem M.Draz Marwa S.Farhan Sarah N.Abdulkader M.G.Gafar Department of Software Engineering Faculty of Computers and InformationKafrelsheikh UniversityKafr ElsheikhEgypt Faculty of Informatics and Computer Science British University in EgyptCairoEgypt Department of Information Systems Faculty of Computers and Artificial IntelligenceHelwan UniversityCairoEgypt Department of Computer Science Faculty of Computers and Artificial IntelligenceHelwan UniversityCairoEgypt Faculty of Computer Studies Arab Open UniversityCairoEgypt Department of Computer Science College of Science and Humanities in Al-SulailPrince Sattam bin Abdulaziz UniversityKharjSaudi Arabia Department of Machine Learning and Information Retrieval Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr ElsheikhEgypt

software systems have been employed in many fields as a means to reduce human efforts;consequently,stakeholders are interested in more updates of their *** smells arise as one of the obstacles in the software *** are characteristics of software source code that indicate a deeper problem in *** smells appear not only in the design but also in software *** smells introduce bugs,affect software maintainability,and lead to higher maintenance *** code smells can be formulated as an optimization problem of finding the best detection *** researchers have recommended different techniques to improve the accuracy of code smell detection,these methods are still unstable and need to be *** research has sought only to discover a few at a time(three or five types)and did not set rules for detecting their *** research improves code smell detection by applying a search-based technique;we use the Whale Optimization Algorithm as a classifier to find ideal detection *** this algorithm,the Fisher criterion is utilized as a fitness function to maximize the between-class distance over the withinclass *** proposed framework adopts if-then detection rules during the software development life *** rules identify the types for both medium and large *** are conducted on five open-source software projects to discover nine smell types that mostly appear in *** proposed detection framework has an average of 94.24%precision and 93.4%*** accurate values are better than other search-based algorithms of the same *** proposed framework improves code smell detection,which increases software quality while minimizing maintenance effort,time,and ***,the resulting classification rules are analyzed to find the software metrics that differentiate the nine code smells.

关键词： software engineering intelligence search-based software engineering code smell detection software metrics whale optimization algorithm fisher criterion

Line Clustering and Contour Extraction in the Context of 2D Building Plans 29

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Line Clustering and Contour Extraction in the Context of 2D ...

29th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, WSCG 2021

作者： Pointner, Andreas Praschl, Christoph Krauss, Oliver Schuler, Andreas Helm, Emmanuel Zwettler, Gerald Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Softwarepark 11 Hagenberg im Mühlkreis 4232 Austria Department of Medical and Bioinformatics School of Informatics Communications and Media University of Applied Sciences Upper Austria Softwarepark 11 Hagenberg im Mühlkreis 4232 Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Softwarepark 11 Hagenberg im Mühlkreis 4232 Austria

ISBN: (纸本)9788086943343

For the purpose of analyzing a building according to its accessibility or structural resilience, printed 2D floor plans are not sufficient because of the missing link to semantic information. This paper tackles this issue and introduces a concept for clustering classified lines of a floor plan and for creating semantically enriched contour elements based on different image processing, computer vision and machine learning algorithms. Based on a general line clustering approach, we introduce type specific methods for walls, windows, doors and stairs. The resulting clusters are in turn used for a contour creation, which uses minimal rotated rectangles. Those rectangles are transformed to polygons that are refined using post processing steps. The approach is evaluated via positive testing using a pixel-based comparison of the process’s result. For this, automatically generated as well as real world building plans are used. The final evaluation shows, that the concept reaches a confidence of >90% for door, stair and windows and only around 10% for stairs with the run-time linearly scaling with the size of the input. © 2021 University of West Bohemia. All rights reserved.

关键词： Machine learning

TIMED AUTOMATA ROBUSTNESS ANALYSIS VIA MODEL CHECKING

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arXiv 2021年

作者： Bendík, Jaroslav Sencan, Ahmet Gol, Ebru Aydin Černá, Ivana Max Planck Institute for Software Systems Kaiserslautern Germany Department of Computer Engineering Middle East Technical University Ankara Turkey Faculty of Informatics Masaryk University Brno Czech Republic

Timed automata (TA) have been widely adopted as a suitable formalism to model time-critical systems. Furthermore, contemporary model-checking tools allow the designer to check whether a TA complies with a system specification. However, the exact timing constants are often uncertain during the design phase. Consequently, the designer is often able to build a TA with a correct structure, however, the timing constants need to be tuned to satisfy the specification. Moreover, even if the TA initially satisfies the specification, it can be the case that just a slight perturbation during the implementation causes a violation of the specification. Unfortunately, model-checking tools are usually not able to provide any reasonable guidance on how to fix the model in such situations. In this paper, we propose several concepts and techniques to cope with the above mentioned design phase issues when dealing with reachability and safety specifications. © 2021, CC BY.

关键词： Specifications

Anytime diagnosis for reconfiguration

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arXiv 2021年

作者： Felfernig, Alexander Walter, Rouven Galindo, José A. Benavides, David Erdeniz, Seda Polat Atas, Müslüm Reiterer, Stefan Applied Software Engineering Group Institute for Software Technology TU Graz Austria Symbolic Computation Group WSI Informatics Universität Tübingen Germany Computer Languages and Systems Department University of Sevilla Spain SelectionArts Austria

Many domains require scalable algorithms that help to determine diagnoses efficiently and often within predefined time limits. Anytime diagnosis is able to determine solutions in such a way and thus is especially useful in real-time scenarios such as production scheduling, robot control, and communication networks management where diagnosis and corresponding reconfiguration capabilities play a major role. Anytime diagnosis in many cases comes along with a trade-off between diagnosis quality and the efficiency of diagnostic reasoning. In this paper we introduce and analyze FLEXDIAG which is an anytime direct diagnosis approach. We evaluate the algorithm with regard to performance and diagnosis quality using a configuration benchmark from the domain of feature models and an industrial configuration knowledge base from the automotive domain. Results show that FLEXDIAG helps to significantly increase the performance of direct diagnosis search with corresponding quality tradeoffs in terms of minimality and accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Scheduling