Journal of Web Engineering最新文献

{"title":"Code Smell-Guided Prompting for LLM-Based Defect Prediction in Ansible Scripts","authors":"Hyunsun Hong;Sungu Lee;Duksan Ryu;Jongmoon Baik","doi":"10.13052/jwe1540-9589.2383","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2383","url":null,"abstract":"Ensuring the reliability of infrastructure as code (IaC) scripts, like those written in Ansible, is vital for maintaining the performance and security of edge-cloud systems. However, the scale and complexity of these scripts make exhaustive testing impractical. To address this, we propose a large language model (LLM)-based software defect prediction (SDP) approach that uses code-smell-guided prompting (CSP). In some cases, CSP enhances LLM performance in defect prediction by embedding specific code smell indicators directly into the prompts. We explore various prompting strategies, including zero-shot, one-shot, and chain of thought CSP (CoT-CSP), to evaluate how code smell information can improve defect detection. Unlike traditional prompting, CSP uniquely leverages code context to guide LLMs in identifying defect-prone code segments. Experimental results reveal that while zero-shot prompting achieves high baseline performance, CSP variants provide nuanced insights into the role of code smells in improving SDP. This study represents exploration of LLMs for defect prediction in Ansible scripts, offering a new perspective on enhancing software quality in edge-cloud deployments.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"23 8","pages":"1107-1126"},"PeriodicalIF":0.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Privacy and Performance in Virtual Reality: The Advantages of Federated Learning in Collaborative Environments","authors":"Daniel Flores-Martin;Francisco Díaz-Barrancas;Pedro J. Pardo;Javier Berrocal;Juan M. Murillo","doi":"10.13052/jwe1540-9589.2382","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2382","url":null,"abstract":"Federated Learning has emerged as a promising approach for maintaining data privacy across distributed environments, enabling training on a diverse range of devices from high-performance servers to low-power gadgets. Despite its potential, managing numerous data sources can strain these devices, particularly those with limited capabilities, leading to increased latency. This is especially critical in virtual reality, where real-time responsiveness is crucial due to the need for constant data connectivity. Historically, virtual reality systems have relied on tethered computer setups, restricting their flexibility and the benefits of wireless technology. However, recent advancements have enhanced the computational power of VR devices, allowing them to perform certain tasks independently. This work explores the feasibility of training a neural network on VR devices, using a federated learning approach, to develop a collaborative model aggregated and stored in the cloud. The goal is to assess the computational demands and explore the potential and constraints of leveraging VR devices for artificial intelligence applications.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"23 8","pages":"1085-1106"},"PeriodicalIF":0.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personalized User Models in a Real-World Edge Computing Environment: A Peer-to-Peer Federated Learning Framework","authors":"Xiangchi Song;Zhaoyan Wang;KyeongDeok Baek;In-Young Ko","doi":"10.13052/jwe1540-9589.2381","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2381","url":null,"abstract":"As the number of IoT devices and the volume of data increase, distributed computing systems have become the primary deployment solution for large-scale Internet of Things (IoT) environments. Federated learning (FL) is a collaborative machine learning framework that allows for model training using data from all participants while protecting their privacy. However, traditional FL suffers from low computational and communication efficiency in large-scale hierarchical cloud-edge collaborative IoT systems. Additionally, due to heterogeneity issues, not all IoT devices necessarily benefit from the global model of traditional FL, but instead require the maintenance of personalized levels in the global training process. Therefore we extend FL into a horizontal peer-to-peer (P2P) structure and introduce our P2PFL framework: efficient peer-to-peer federated learning for users (EPFLU). EPFLU transitions the paradigms from vertical FL to a horizontal P2P structure from the user perspective and incorporates personalized enhancement techniques using private information. Through horizontal consensus information aggregation and private information supplementation, EPFLU solves the weakness of traditional FL that dilutes the characteristics of individual client data and leads to model deviation. This structural transformation also significantly alleviates the original communication issues. Additionally, EPFLU has a customized simulation evaluation framework, and uses the EUA dataset containing real-world edge server distribution, making it more suitable for real-world large-scale IoT. Within this framework, we design two extreme data distribution scenarios and conduct detailed experiments of EPFLU and selected baselines on the MNIST and CIFAR-10 datasets. The results demonstrate that the robust and adaptive EPFLU framework can consistently converge to optimal performance even under challenging data distribution scenarios. Compared with the traditional FL and selected P2PFL methods, EPFLU achieves communication time improvements of 39% and 16% respectively.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"23 8","pages":"1057-1083"},"PeriodicalIF":0.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10879171","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Machine Learning Systems in Edge Cloud Environments","authors":"In-Young Ko;Michael Mrissa;Juan Manuel Murillo;Abhishek Srivastava","doi":"","DOIUrl":"https://doi.org/","url":null,"abstract":"The international workshop on Big Data-Driven Edge Cloud Services (BECS) aims to provide a platform for scholars and practitioners to share their experiences and present ongoing work in developing data-driven AI applications and services within distributed computing environments, commonly referred to as the edge cloud.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"23 8","pages":"v-vii"},"PeriodicalIF":0.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10879110","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Overcoming Terrain Challenges with Edge Computing Solutions: Optimizing WSN Deployments Over Obstacle Clad-Irregular Terrains","authors":"Shekhar Tyagi;Abhishek Srivastava","doi":"10.13052/jwe1540-9589.2384","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2384","url":null,"abstract":"Wireless sensor networks (WSNs) are primarily used for real time data collection and monitoring, especially in environments where direct human involvement is challenging due to harsh conditions. Optimized deployment of WSN nodes is a long standing issue and several ideas have been proposed to address this. Existing deployment strategies are mostly based on the assumption that the terrain for deployment of nodes is perfectly regular. This is an impractical assumption and in this paper we address this gap by proposing a deployment strategy for WSN nodes over irregular terrains. Such terrains comprise uneven elevations, morphology and vegetation based obstacles, rocky obstacles, and so on. Our approach comprises extraction of satellite images of the region of interest (RoI) from Google Earth and generating a KML file (Keyhole Markup Language) for the RoI containing the latitude, longitude, and elevation values of each and every point in the RoI. These points are used to generate a contour map of the RoI containing detailed terrain morphology. A radio frequency path loss model in combination with an advanced inverse distance weighted (IDW)-interpolation technique is proposed to ensure connectivity and coverage in such irregular terrains with varying nature of obstacles. The technique effectively detects occlusions and enables effective deployment. This edge computing approach involves real-time decision-making at the network edge (the sensor nodes) leading to a deterministic deployment of motes in diverse terrain conditions with various obstacles. The approach is compared with existing deployment techniques and the results validate its efficacy. To demonstrate the practicality of our approach, we have also implemented a deployment in real-world environmental conditions, validating our approach in challenging terrains.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"23 8","pages":"1127-1154"},"PeriodicalIF":0.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Software Practice and Experience on Smart Mobility Digital Twin in Transportation and Automotive Industry: Toward SDV-Empowered Digital Twin Through EV Edge-Cloud and AutoML","authors":"Jonggu Kang","doi":"10.13052/jwe1540-9589.2385","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2385","url":null,"abstract":"A digital twin is a virtual representation of a physical asset that serves as a pivotal convergence technology that facilitates real-time prediction, optimization, monitoring, control, and improved decision-making. It can be widely applied to various domains, such as automotive, manufacturing, logistics, and smart cities. The automotive industry, in particular, is actively integrating digital twins throughout the product life cycle, from research and development, production, sales, and services to enhance the overall customer experience. This paper presents insights and lessons learned on software practice and experience related to implementing smart mobility digital twins, focusing on the potential of transportation digital twins built from data collected by electric vehicles (EVs) with EV edge cloud and automated machine learning (AutoML). Despite current limitations in data sufficiency, we forecast that, as the SDV trend accelerates and the adoption of EVs increases, the digital twin will become essential for the intelligent transportation system (ITS) in future smart cities, enabling accurate traffic predictions even in areas with limited road infrastructure. The successful integration of real-time data, high-performance prediction models, and automated service environments will enhance the effectiveness toward an SDV edge-empowered transportation digital twin.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"23 8","pages":"1155-1180"},"PeriodicalIF":0.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Web Intelligent Hotel Management Framework Based on IoT and Generative AI","authors":"Ling Luo","doi":"10.13052/jwe1540-9589.2371","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2371","url":null,"abstract":"The hotel industry has faced numerous opportunities and challenges due to the advent of the artificial intelligence (AI) and the data-driven era. To address this, a novel augmented online performance analysis model is proposed for hotel management operations. This model seamlessly integrates the Internet of Things (IoT), generative AI technologies, and web engineering, allowing for the collection and analysis of multifaceted operational data. Consequently, real-time insights pertaining to room reservations, occupancy rates, and revenue streams are derived, serving as the basis for data-driven optimization strategies. Moreover, by incorporating generative AI technologies, the proposed model demonstrates the ability to dynamically generate predictive models, simulate scenarios, synthesize actionable insights, and adapt to evolving trends. As a result, it offers adaptive solutions for complex hotel management scenarios that were previously beyond the reach of traditional methods.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"23 7","pages":"885-912"},"PeriodicalIF":0.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10815717","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Effective Scheme to Accelerate NeRF for Web Applications Using Hash-Based Caching and Precomputed Features","authors":"OkHwan Bae;Chung-Pyo Hong","doi":"10.13052/jwe1540-9589.2376","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2376","url":null,"abstract":"In recent years, 3D reconstruction and rendering technologies have become increasingly important in various web-based applications within the field of web technology. In particular, with the emergence of technologies such as WebGL and WebGPU, which enable real-time 3D content rendering in web browsers, immersive experiences and interactions on the web have been significantly enhanced. These technologies are widely used in applications such as 3D visualization of virtual products or 3D exploration of building interiors on real estate websites. Through these advancements, users can experience 3D content directly in their browsers without the need to install additional software, greatly expanding the possibilities of the web. Amidst this trend, the neural radiance field (NeRF) has garnered attention as a cutting-edge technology that improves the accuracy of 3D reconstruction and rendering. NeRF is a technique widely used in computer vision and graphics for reconstructing 3D spaces from 2D images taken from multiple viewpoints. By predicting the color and density of each pixel, NeRF captures the complex 3D structure and optical properties of a scene, enabling highly accurate 3D reconstructions. However, NeRF's primary limitation is the time-consuming nature of both the training and inference processes. Research efforts to address this issue have focused on two key areas: optimizing network architectures and training procedures to accelerate scene learning, and improving inference speed for faster rendering. While progress has been made in enhancing training speed, challenges remain in improving the inference process. To address these limitations, we propose a two-step approach to significantly improve NeRF's performance. First, we optimize the training phase through a multi-resolution hash encoding technique, reducing the computational complexity and speeding up the learning process. Second, we accelerate the inference phase by caching the input data of the NeRF MLP, which allows for faster rendering without sacrificing quality. Our experimental results demonstrate that this approach reduces training time by 68.42% and increases inference speed by 98.18%.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"23 7","pages":"1041-1056"},"PeriodicalIF":0.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10815716","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cluster-Based Data Sharing for Web 3.0 in Intelligent Transportation Systems","authors":"Mohammed Alkhathami","doi":"10.13052/jwe1540-9589.2375","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2375","url":null,"abstract":"Intelligent transportation system (ITS) applications are dependent on secure and robust wireless data sharing among vehicles and roadside units (RSUs). Multiple types of data are shared among the ITS devices which include safety information, road services, web based information retrieval and task computation. Web 3.0 offers a decentralized, distributed and secure data sharing mechanism for ITSs. Allocation of wireless channel resources are critical to enable an efficient ITS system. In this paper, a novel data sharing technique for Web 3.0 based ITS is presented that relies on an intelligent clustering algorithm. In the first step, the proposed technique uses a K-means algorithm to find groups of vehicles with similar speeds. In the second step, each cluster is assigned an RSU which has the highest average data rate with all vehicles in the cluster. This is achieved by using a stable matching technique so that there is no contention and each cluster is assigned a separate RSU. The algorithm periodically updates the clusters and RSU allocation for web data sharing between vehicles and RSUs. Simulation results show that the proposed clustering-based data sharing technique improves sum-rate by 20% and reduces network delay by 23%.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"23 7","pages":"1025-1040"},"PeriodicalIF":0.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10815633","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Data Alignment Method for Network Packet Capture Based on DBSCAN","authors":"Jiarui Lu;Qinggang Su","doi":"10.13052/jwe1540-9589.2374","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2374","url":null,"abstract":"This paper investigates the issues of packet alignment and consistency among PLC devices based on industrial network environments, aiming to ensure the integrity and accuracy of packets from sender to receiver. To achieve this goal, we propose an anomaly detection method that combines the DBSCAN clustering algorithm with the 3-sigma principle to identify and handle abnormal packets that may occur during transmission. By comparing the data between the sending and receiving ends, and analyzing based on timestamps and data content, we validate the alignment of packets in the network environment. Experimental results demonstrate that the proposed method effectively detects and corrects packet loss or delay jitter, thereby enhancing the reliability of communication between PLC devices and the consistency of data transmission. The scheme presented in this paper enables quicker and more precise identification of packet loss and delays, adapting well to various network load conditions. Further experimental analysis indicates that this method excels in reducing both false positive and false negative rates, and it exhibits good scalability, making it applicable to data alignment and consistency verification in other industrial automation scenarios. Ultimately, this novel solution provides stability and accuracy for data transmission among devices in a network environment.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"23 7","pages":"1003-1023"},"PeriodicalIF":0.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10815714","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}