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PATCH: Empowering Large Language Model with Programmer-Intent Guidance and Collaborative-Behavior Simulation for Automatic Bug Fixing

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ACM Transactions on software engineering and Methodology 1000年

作者： Yuwei Zhang Zhi Jin Ying Xing Ge Li Fang Liu Jiaxin Zhu Wensheng Dou Jun Wei Key Laboratory of System Software (Chinese Academy of Sciences) Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences China Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of Education School of Computer Science Peking University School of Computer Science Wuhan University China School of Intelligent Engineering and Automation Beijing University of Posts and Telecommunications China School of Computer Science Peking University China State Key Laboratory of Complex & Critical Software Environment School of Computer Science and Engineering Beihang University China

Bug fixing holds significant importance in software development and maintenance. Recent research has made substantial strides in exploring the potential of large language models (LLMs) for automatically resolving software bugs. However, a noticeable gap in existing approaches lies in the oversight of collaborative facets intrinsic to bug resolution, treating the process as a single-stage endeavor. Moreover, most approaches solely take the buggy code snippet as input for LLMs during the patch generation stage. To mitigate the aforementioned limitations, we introduce a novel stage-wise framework named PATCH. Specifically, we first augment the buggy code snippet with corresponding dependence context and intent information to better guide LLMs in generating the correct candidate patches. Additionally, by taking inspiration from bug management practices, we decompose the bug-fixing task into four distinct stages: bug reporting, bug diagnosis, patch generation, and patch verification. These stages are performed interactively by LLMs, aiming to simulate the collaborative behavior of programmers during the resolution of software bugs. By harnessing these collective contributions, PATCH effectively enhances the bug-fixing capability of LLMs. We implement PATCH by employing the powerful dialogue-based LLM ChatGPT. Our evaluation on the widely used bug-fixing benchmark BFP demonstrates that PATCH has achieved better performance than state-of-the-art LLMs.

关键词： Bug Fixing Large Language Model Bug Management Multi-Agent Collaboration

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Efficient Training Approaches for Performance Anomaly Detection Models in Edge Computing Environments

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ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Duneesha Fernando Maria Rodriguez Patricia Arroba Leila Ismail Rajkumar Buyya Quantum Cloud Computing and Distributed Systems (qCLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne Australia CCS–Center for Computational Simulation Universidad Politécnica de Madrid Spain Intelligent Distributed Computing and Systems Research (INDUCE) Lab Department of Computer Science and Software Engineering United Arab Emirates University United Arab Emirates

Microservice architectures are increasingly used to modularize IoT applications and deploy them in distributed and heterogeneous edge computing environments. Over time, these microservice-based IoT applications are susceptible to performance anomalies caused by resource hogging (e.g., CPU or memory), resource contention, etc., which can negatively impact their Quality of Service and violate their Service Level Agreements. Existing research on performance anomaly detection for edge computing environments focuses on model training approaches that either achieve high accuracy at the expense of a time-consuming and resource-intensive training process or prioritize training efficiency at the cost of lower accuracy. To address this gap, while considering the resource constraints and the large number of devices in modern edge platforms, we propose two clustering-based model training approaches: (1) intra-cluster parameter transfer learning-based model training (ICPTL) and (2) cluster-level model training (CM). These approaches aim to find a trade-off between the training efficiency of anomaly detection models and their accuracy. We compared the models trained under ICPTL and CM to models trained for specific devices (most accurate, least efficient) and a single general model trained for all devices (least accurate, most efficient). Our findings show that ICPTL’s model accuracy is comparable to that of the model per device approach while requiring only 40% of the training time. In addition, CM further improves training efficiency by requiring 23% less training time and reducing the number of trained models by approximately 66% compared to ICPTL, yet achieving a higher accuracy than a single general model.

关键词： Edge computing Microservices IoT Performance anomaly detection Machine learning Deep learning

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Multi-Agent-Based Simulation II 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Jaime Simão Sichman Françcois Bousquet Paul Davidsson

ISBN: (数字)9783540364832

ISBN: (纸本)9783540006077

This volume presents extended and revised versions of the papers presented at the Third International Workshop on Multi-Agent Based Simulation (MABS 2002), a workshop federated with the First International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2002), which was held in Bologna, Italy, in July, 2002. This workshop was the third in the MABS series. The earlier two were - ganized as workshops of the two most recent ICMAS conferences (ICMAS 1998, Paris, France and ICMAS 2000, Boston, USA). Revised versions of the papers presented at these workshops were published as volumes 1534 and 1979 in the Lecture Notes in Arti?cial Intelligence series. One aim of the workshop was to develop stronger links between those wo- ing in the social sciences and those involved with multi-agent systems. We are pleased to note that many important conferences in various disciplines such as geography, economics, ecology, sociology, and physics have hosted workshops on MABS-related topics and that many respected journals publish papers that - clude elements of MABS. But although MABS is gradually acquiring legitimacy in many disciplinary ?elds, much remains to be done to clarify the potential use of MABS in these disciplines. Researchers from these disciplines have di?erent points of view on issues such as time-frame, space, geographical scales, or- nizational levels, etc. Moreover, the interest in MABS goes beyond the scienti?c community, as MABS models have been developed and used interactively with other communities as well.

关键词： Simulation and Modeling Artificial Intelligence Computer Communication Networks Information Systems Applications (incl. Internet) User Interfaces and Human Computer Interaction Computer Appl. in Social and Behavioral Sciences

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Compression Approaches for LiDAR Point Clouds and Beyond: A Survey

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ACM Transactions on Multimedia Computing, Communications, and Applications 1000年

作者： Miaohui Wang Runnan Huang Wuyuan Xie Zhan Ma Siwei Ma Guangdong Key Laboratory of Intelligent Information Processing State Key Laboratory of Radio Frequency Heterogeneous Integration Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China School of Electronic Science and Engineering Nanjing University China School of Computer Science Peking University China

With the widespread use of LiDAR sensors in autonomous driving, LiDAR point cloud compression (LPCC) plays an important role in effectively managing the storage, transmission, and perception of the growing volume of LiDAR data. Despite this need, there has been a noticeable absence of comprehensive investigations specifically dedicated to LPCC methods. To address this issue, this paper presents a systematic survey of existing LPCCs, aiming to summarize recent progress and inspire future research in this field. We begin by providing a general introduction of LPCC fundamentals, covering the latest LiDAR point cloud (LPC) datasets, distinctive attributes, evaluation metrics, and data formats. We then conduct a careful review and comparison of LPCCs, examining image-based, octree-based, deep-learned, and other approaches, offering valuable insights into the strengths and weaknesses of cutting-edge models. Finally, we propose future research directions based on the limitations of recent LPCCs. We believe that the findings presented in this paper will contribute to a deeper understanding of LPCCs and promote further development of LiDAR sensor-based systems.

关键词： LiDAR point cloud compression LiDAR data attributes LiDAR coding survey

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Multi-Agent Reinforcement Learning based Edge Content Caching for Connected Autonomous Vehicles in IoV

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ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Xiaolong Xu Linjie Gu Muhammad Bilal Maqbool Khan Yiping Wen Guoqiang Liu Yuan Yuan School of Software Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing University of Information Science and Technology China Changwang School of Honors Nanjing University of Information Science and Technology China Department of Computer and Electronics Systems Engineering Hankuk University of Foreign Studies Korea Department of IT and Computer Science Pak-Austria Fachhochschule-Institute of Applied Sciences and Technology Pakistan School of Computer Science and Engineering Hunan University of Science and Technology China School of Software Nanjing University of Information Science and Technology China School of Computer Science and Engineering Beihang University State Key Laboratory of Software Development Environment Zhongguancun Laboratory China

Connected Autonomous Vehicle (CAV) Driving, as a data-driven intelligent driving technology within the Internet of Vehicles (IoV), presents significant challenges to the efficiency and security of real-time data management. The combination of Web3.0 and edge content caching holds promise in providing low-latency data access for CAVs’ real-time applications. Web3.0 enables the reliable pre-migration of frequently requested content from content providers to edge nodes. However, identifying optimal edge node peers for joint content caching and replacement remains challenging due to the dynamic nature of traffic flow in IoV. Addressing these challenges, this article introduces GAMA-Cache, an innovative edge content caching methodology leveraging Graph Attention Networks (GAT) and Multi-Agent Reinforcement Learning (MARL). GAMA-Cache conceptualizes the cooperative edge content caching issue as a constrained Markov decision process. It employs a MARL technique predicated on cooperation effectiveness to discern optimal caching decisions, with GAT augmenting information extracted from adjacent nodes. A distinct collaborator selection mechanism is also developed to streamline communication between agents, filtering out those with minimal correlations in the vector input to the policy network. Experimental results demonstrate that, in terms of service latency and delivery failure, the GAMA-Cache outperforms other state-of-the-art MARL solutions for edge content caching in IoV.

关键词： Content Caching Reinforcement Learning Connected Autonomous Driving

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An X Language-Driven Framework for Systematic Development of Digital Twin Healthcare Systems

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ACM Transactions on Multimedia Computing, Communications, and Applications 1000年

作者： Kunyu Xie Lin Zhang Yuan Yang Xiaohe Li Ridha Khedri Zhen Chen M. Jamal Deen Simulation Technology Innovation Center Hangzhou International Innovation Institute of Beihang University Hangzhou 311115 Zhejiang China School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China State Key Laboratory of Intelligent Manufacturing Systems Technology Beijing 100191 China School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China Shenzhen Third People’s Hospital & Second Hospital Affiliated to Southern University of Science and Technology Shenzhen 518112 Guangdong China Dept. of Computing and Software McMaster University Hamilton Ontario Canada L8S 4K1 Department of Electrical and Computer Engineering McMaster University Hamilton ON L8S 4L8 Canada

The rapid advancements in big data and the Internet of Things (IoT) have significantly accelerated the digital transformation of medical institutions, leading to the widespread adoption of Digital Twin Healthcare (DTH). The Cloud DTH Platform (CDTH) serves as a cloud-based framework that integrates DTH models, healthcare resources, patient data, and medical services. By leveraging real-time data from medical devices, the CDTH platform enables intelligent healthcare services such as disease prediction and medical resource optimization. However, the platform functions as a system of systems (SoS), comprising interconnected yet independent healthcare services. This complexity is further compounded by the integration of both black-box AI models and domain-specific mechanistic models, which pose challenges in ensuring the interpretability and trustworthiness of DTH models. To address these challenges, we propose a Model-Based Systems engineering (MBSE)-driven DTH modeling methodology derived from systematic requirement and functional analyses. To implement this methodology effectively, we introduce a DTH model development approach using the X language, along with a comprehensive toolchain designed to streamline the development process. Together, this methodology and toolchain form a robust framework that enables engineers to efficiently develop interpretable and trustworthy DTH models for the CDTH platform. By integrating domain-specific mechanistic models with AI algorithms, the framework enhances model transparency and reliability. Finally, we validate our approach through a case study involving elderly patient care, demonstrating its effectiveness in supporting the development of DTH models that meet healthcare and interpretability requirements.

关键词： Digital Twin Model-based system engineering X Language Medical Cyber-Physical Systems Digital Twin Healthcare Design

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Feature Disentanglement Based Heterogeneous Defect Prediction

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ACM Transactions on software engineering and Methodology 1000年

作者： Xu Yu Jiaqi Yan Qinqin Gao Qinglong Peng Bin Yu Junwei Du Ying Xing Dunwei Gong Qingdao Institute of Software China University of Petroleum East China) Qingdao China College of Computer Science and Technology China University of Petroleum East China) Qingdao China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software China University of Petroleum East China) Qingdao China School of Data Science Qingdao University of Science and Technology Qingdao China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Changchun China School of Data Science Qingdao University of Science and Technology Qingdao China School of Computer and Information Technology Beijing Jiaotong University Beijing China School of Data Science Qingdao University of Science and TechnologyQingdao China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Yunnan Key Laboratory of Software Engineering Kunming China College of Automation and Electronic Engineering Qingdao University of Science and Technology Qingdao China

Cross-project defect prediction (CPDP) utilizes the existing labeled data in the source project to assist with the prediction of unlabeled projects in the target dataset, which effectively improves the prediction performance and has become a research hotspot in software engineering. At present, CPDP can be categorized into homogeneous cross-project defect prediction and heterogeneous cross-project defect prediction (HDP), in which HDP doesn’t require that the source project and the target project have the same feature space, thus, it is more widely used in the actual CPDP. Most of current HDP methods map the original features to the latent feature space and reduce the inter-project variation by transferring domain-independent features, but the transferring process ignores the use of domain-related features, which affects the prediction performance of the model. Moreover, the mapped latent features are not conducive to the model’s interpretability. Based on these, this paper proposes a heterogeneous defect prediction method based on feature disentanglement (FD-HDP). We disentangle the features using domain-related and domain-independent feature extractors, respectively, to improve the interpretability of the model by maximizing the domain adversarial loss during training and guiding the feature extractors to produce accurate domain-related and domain-independent features. The weighted sum of the prediction results from domain-related and domain-independent predictors is used as the final prediction result of the project during the prediction process, which realizes the combination of domain-independent and domain-related features and effectively improves the prediction performance. In this paper, we conducted experiments using four publicly available defect datasets to construct heterogeneous scenarios. The results demonstrate that the FD-HDP model shows significant advantages over state-of-the-art methods in six metrics.

关键词： Heterogeneous Defect Prediction Feature Disentanglement Domain-related Features Domain-independent Features

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Vehicle Dynamics and Interaction for Trajectory Prediction and Traffic Control

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ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Jian Chen Shaorui Zhou Wei Wang Yuzhu Hu Jianqing Li Ben-Guo He Junxin Chen Marwan Omar Ali Kashif Bashir Xiping Hu School of Intelligent Systems Engineering Sun Yat-Sen University China Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology Macao SAR China Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Northeastern University China the School of Software Dalian University of Technology China Information Technology and Management Illinois Institute of Technology USA Department of Computing and Mathematics Manchester Metropolitan University UK Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India

Trajectory prediction is a crucial challenge in autonomous vehicle motion planning and decision-making techniques. However, existing methods face limitations in accurately capturing vehicle dynamics and interactions. To address this issue, this paper proposes a novel approach to extracting vehicle velocity and acceleration, enabling the learning of vehicle dynamics and encoding them as auxiliary information. The VDI-LSTM model is designed, incorporating graph convolution and attention mechanisms to capture vehicle interactions using trajectory data and dynamic information. Specifically, a dynamics encoder is designed to capture the dynamic information, a dynamic graph is employed to represent vehicle interactions, and an attention mechanism is introduced to enhance the performance of LSTM and graph convolution. To demonstrate the effectiveness of our model, extensive experiments are conducted, including comparisons with several baselines and ablation studies on real-world highway datasets. Experimental results show that VDI-LSTM outperforms other baselines compared, which obtains a 3% improvement on the average RMSE indicator over the five prediction steps.

关键词： Trajectory Prediction Vehicle Dynamics Interaction Perception Graph Convolution Attention Mechanism

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Model Pruning-enabled Federated Split Learning for Resource-constrained Devices in Artificial Intelligence Empowered Edge Computing Environment

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ACM Transactions on Sensor Networks 1000年

作者： Yongzhe Jia Bowen Liu Xuyun Zhang Fei Dai Arif Khan Lianyong Qi Wanchun Dou State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China Department of Computing Macquarie University Sydney Australia College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland College of Computer Science and Technology China University of Petroleum East China - Qingdao Campus Qingdao China

Distributed Collaborative Machine Learning (DCML) has emerged in artificial intelligence-empowered edge computing environments, such as the Industrial Internet of Things (IIoT), to process tremendous data generated by smart devices. However, parallel DCML frameworks require resource-constrained devices to update the entire Deep Neural Network (DNN) models and are vulnerable to reconstruction attacks. Concurrently, the serial DCML frameworks suffer from training efficiency problems due to their serial training nature. In this paper, we propose a Model Pruning-enabled Federated Split Learning framework (MP-FSL) to reduce resource consumption with a secure and efficient training scheme. Specifically, MP-FSL compresses DNN models by adaptive channel pruning and splits each compressed model into two parts that are assigned to the client and the server. Meanwhile, MP-FSL adopts a novel aggregation algorithm to aggregate the pruned heterogeneous models. We implement MP-FSL with a real FL platform to evaluate its performance. The experimental results show that MP-FSL outperforms the state-of-the-art frameworks in model accuracy by up to 1.35%, while concurrently reducing storage and computational resource consumption by up to 32.2% and 26.73%, respectively. These results demonstrate that MP-FSL is a comprehensive solution to the challenges faced by DCML, with superior performance in both reduced resource consumption and enhanced model performance.

关键词： Federated learning split learning model pruning edge computing

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Distributed Computation Offloading and Power Control for UAV-Enabled Internet of Medical Things

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ACM Transactions on Internet Technology 1000年

作者： Jiakun Gao Xiaolong Xu Lianyong Qi Wanchun Dou Xiaoyu Xia Xiaokang Zhou School of Software Nanjing University of Information Science and Technology NanJing China School of Software Nanjing University of Information Science and Technology NanJing China and Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET) Nanjing University of Information Science and Technology Nanjing China College of Computer Science and Technology China University of Petroleum (East China) QingDao China and State Key Laboratory for Novel Software Technology Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Technology RMIT University Victoria Australia Faculty of Data Science Shiga University Hikone Japan

The advancement of the Internet of Medical Things (IoMT) has led to the emergence of various health and emotion care services, e.g., health monitoring. To cater to increasing computational requirements of IoMT services, Mobile Edge Computing (MEC) has emerged as an indispensable technology in smart health. Benefiting from the cost-effectiveness of deployment, unmanned aerial vehicles (UAVs) equipped with MEC servers in Non-Orthogonal Multiple Access (NOMA) have emerged as a promising solution for providing smart health services in proximity to medical devices (MDs). However, the escalating number of MDs and the limited availability of communication resources of UAVs give rise to a significant increase in transmission latency. Moreover, due to the limited communication range of UAVs, the geographically-distributed MDs lead to workload imbalance of UAVs, which deteriorates the service response delay. To this end, this paper proposes a UAV-enabled Distributed computation Offloading and Power control method with Multi-Agent, named DOPMA, for NOMA-based IoMT environment. Specifically, this paper introduces computation and transmission queue models to analyze the dynamic characteristics of task execution latency and energy consumption. Moreover, a credit assignment scheme-based reward function is designed considering both system-level rewards and rewards tailored to each MD, and an improved multi-agent deep deterministic policy gradient algorithm is developed to derive offloading and power control decisions independently. Extensive simulations demonstrate that the proposed method outperforms existing schemes, achieving \(7.1\%\) reduction in energy consumption and \(16\%\) decrease in average delay.

关键词： Internet of Medical Things (IoMT) Mobile Edge Computing (MEC) Unmanned Aerial Vehicles (UAV) Non-Orthogonal Multiple Access (NOMA)

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