检索结果-内蒙古大学图书馆

Are We Building on the Rock? On the Importance of Data Preprocessing for Code Summarization

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Shi, Lin Mu, Fangwen Chen, Xiao Wang, Song Wang, Junjie Yang, Ye Li, Ge Xia, Xin Wang, Qing Institute of Software Chinese Academy of Sciences Beijing China Lassonde School of Engineering York University Toronto Canada School of Systems and Enterprises Stevens Institute of Technology HobokenNJ United States Key Lab of High Confidence Software Technology Peking University Beijing China Software Engineering Application Technology Lab Huawei China Laboratory for Internet Software Technologies Institute of Software CAS China University of Chinese Academy of Sciences China Science & Technology on Integrated Information System Laboratory Institute of Software CAS China

Code summarization, the task of generating useful comments given the code, has long been of interest. Most of the existing code summarization models are trained and validated on widely-used code comment benchmark datasets. However, little is known about the quality of the benchmark datasets built from real-world projects. Are the benchmark datasets as good as expected? To bridge the gap, we conduct a systematic research to assess and improve the quality of four benchmark datasets widely used for code summarization tasks. First, we propose an automated code-comment cleaning tool that can accurately detect noisy data caused by inappropriate data preprocessing operations from existing benchmark datasets. Then, we apply the tool to further assess the data quality of the four benchmark datasets, based on the detected noises. Finally, we conduct comparative experiments to investigate the impact of noisy data on the performance of code summarization models. The results show that these data preprocessing noises widely exist in all four benchmark datasets, and removing these noisy data leads to a significant improvement on the performance of code summarization. We believe that the findings and insights will enable a better understanding of data quality in code summarization tasks, and pave the way for relevant research and practice. Copyright © 2022, The Authors. All rights reserved.

关键词： Benchmarking

Towards Efficient Workflow Scheduling Over Yarn Cluster Using Deep Reinforcement Learning

学校读者我要写书评

暂无评论

Towards Efficient Workflow Scheduling Over Yarn Cluster Usin...

IEEE Conference on Global Communications (GLOBECOM)

作者： Jianguo Xue Ting Wang Puyu Cai MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Lab. of Trustworthy Computing East China Normal University China Department of Computer Science and Engineering Michigan State University USA

Hadoop Yarn is an open-source cluster manager responsible for resource management and job scheduling. However, data-driven applications are typically organized into workflows that consist of a series of jobs with dependencies. Yarn does not manage users' workflows and only considers the current job rather than the entire workflow when scheduling. In practice, multiple workflows share the same Yarn cluster and are pre-assigned separate Yarn resource queues to avoid mutual interference. However, this coarse-grained resource division can sometimes result in low resource utilization and increased pending time of jobs on the Yarn queue. For instance, one resource queue may have exhausted its quota while still having pending jobs, while other queues may have available resources but cannot begin executing any jobs due to unfulfilled data dependencies. To address this problem, we propose a deep reinforcement learning-based workflow scheduling scheme that takes into account job dependencies, job priorities, and dynamic resource usage. The proposed approach can intelligently identify and utilize free windows of different resource queues. Our simulation results demonstrate that the proposed DRL-based workflow scheduling scheme can significantly reduce the average job latency compared to existing approaches.

关键词：

Green Federated Learning over Cloud-RAN with Limited Fronthaul and Quantized Neural Networks

学校读者我要写书评

暂无评论

Green Federated Learning over Cloud-RAN with Limited Frontha...

Communications and Networking (MeditCom), IEEE International Mediterranean Conference on

作者： Jiali Wang Yijie Mao Ting Wang Yuanming Shi MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai Key Lab. of Trustworthy Computing East China Normal University China School of Information Science and Technology (SIST) ShanghaiTech University China

In this paper, we investigate a green federated learning (FL) framework over cloud radio access network (Cloud-RAN) system that comprises a server, multiple devices and remote radio heads (RRHs). Each device utilizes quantized neural networks (QNNs) and sends quantized model parameters to the server to save energy consumption via RRHs. The server aggregates all the signals to update the global model parameters and broadcasts the updated parameters to the selected devices. In this context, we propose an energy consumption model for the QNN training and communication model over Cloud-RAN. We develop an energy minimization problem based on the proposed energy model. We jointly design fronthaul rate allocation, device transmit power, and precision level of QNNs while ensuring target accuracy, transmit power budget and limited fronthaul capacity. Guided by the convergence analysis, we adopt alternative optimization method to solve the energy minimization problem. The simulation outcomes demonstrate that the FL framework suggested can considerably diminish energy usage in comparison to other traditional methods. This has immense potential in realizing a sustainable and eco-friendly FL over Cloud-RAN.

关键词：

Prism: Revealing Hidden Functional Clusters from Massive Instances in Cloud Systems 23

学校读者我要写书评

暂无评论

Prism: Revealing Hidden Functional Clusters from Massive Ins...

Proceedings of the 38th IEEE/ACM International Conference on Automated software engineering

作者： Jinyang Liu Zhihan Jiang Jiazhen Gu Junjie Huang Zhuangbin Chen Cong Feng Zengyin Yang Yongqiang Yang Michael R. Lyu The Chinese University of Hong Kong Hong Kong SAR China School of Software Engineering Sun Yat-sen University Zhuhai China Computing and Networking Innovation Lab Huawei Cloud Computing Technology Co. Ltd China

ISBN: (纸本)9798350329964

Ensuring the reliability of cloud systems is critical for both cloud vendors and customers. Cloud systems often rely on virtualization techniques to create instances of hardware resources, such as virtual machines. However, virtualization hinders the observability of cloud systems, making it challenging to diagnose platform-level issues. To improve system observability, we propose to infer functional clusters of instances, i.e., groups of instances having similar functionalities. We first conduct a pilot study on a large-scale cloud system, i.e., huawei Cloud, demonstrating that instances having similar functionalities share similar communication and resource usage patterns. Motivated by these findings, we formulate the identification of functional clusters as a clustering problem and propose a non-intrusive solution called Prism. Prism adopts a coarse-to-fine clustering strategy. It first partitions instances into coarse-grained chunks based on communication patterns. Within each chunk, Prism further groups instances with similar resource usage patterns to produce fine-grained functional clusters. Such a design reduces noises in the data and allows Prism to process massive instances efficiently. We evaluate Prism on two datasets collected from the real-world production environment of huawei Cloud. Our experiments show that Prism achieves a v-measure of ~0.95, surpassing existing state-of-the-art solutions. Additionally, we illustrate the integration of Prism within monitoring systems for enhanced cloud reliability through two real-world use cases.

关键词： functional clusters

Model Checking Quantum Continuous-Time Markov Chains 32

学校读者我要写书评

暂无评论

Model Checking Quantum Continuous-Time Markov Chains

32nd International Conference on Concurrency Theory, CONCUR 2021

作者： Xu, Ming Mei, Jingyi Guan, Ji Yu, Nengkun MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Lab of Trustworthy Computing East China Normal University Shanghai China Shanghai Key Lab of Trustworthy Computing East China Normal University Shanghai China State Key Lab of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney Australia

ISBN: (纸本)9783959772037

Verifying quantum systems has attracted a lot of interests in the last decades. In this paper, we initialise the model checking of quantum continuous-time Markov chain (QCTMC). As a real-time system, we specify the temporal properties on QCTMC by signal temporal logic (STL). To effectively check the atomic propositions in STL, we develop a state-of-the-art real root isolation algorithm under Schanuel's conjecture;further, we check the general STL formula by interval operations with a bottom-up fashion, whose query complexity turns out to be linear in the size of the input formula by calling the real root isolation algorithm. A running example of an open quantum walk is provided to demonstrate our method. © Ming Xu, Jingyi Mei, Ji Guan, and Nengkun Yu;.

关键词： Model checking

Defect Identification, Categorization, and Repair: Better Together

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Ni, Chao Yang, Kaiwen Xia, Xin Lo, David Chen, Xiang Yang, Xiaohu School of Software Technology Zhejiang University Zhejiang Hangzhou310007 China College of Computer Science and Technology Zhejiang University Hangzhou China Software Engineering Application Technology Lab Huawei Hangzhou China Singapore Management University Singapore School of Information Science and Technology Nantong University Nantong China Computer Science and Technology Zhejiang University Hangzhou China

Just-In-Time defect prediction (JIT-DP) models can identify defect-inducing commits at check-in time. Even though previous studies have achieved a great progress, these studies still have the following limitations: 1) useful information (e.g., semantic information and structure information) are not fully used;2) existing work can only predict a commit as buggy one or clean one without more information about what type of defect it is;3) a commit may involve changes in many files, which cause difficulty in locating the defect;4) prior studies treat defect identification and defect repair as separate tasks, none aims to handle both tasks simultaneously. In this paper, to handle aforementioned limitations, we propose a comprehensive defect prediction and repair framework named CompDefect, which can identify whether a changed function (a more fine-grained level) is defect-prone, categorize the type of defect, and repair such a defect automatically if it falls into several scenarios, e.g., defects with single statement fixes, or those that match a small set of defect templates. Generally, the first two tasks in CompDefect are treated as a multiclass classification task, while the last one is treated as a sequence generation task. The whole input of CompDefect consists of three parts (exampled with positive functions): the clean version of a function (i.e., the version before defect introduced), the buggy version of a function and the fixed version of a function. In multiclass classification task, CompDefect categorizes the type of defect via multiclass classification with the information in both the clean version and the buggy version. In code sequence generation task, CompDefect repairs the defect once identified or keeps it unchanged. To verify the effectiveness of CompDefect, we first build a large-scale function-level dataset named Function-SStuBs4J, which totally contains 21,047 instances with three versions of modified functions and we then evaluate CompDefect with

关键词： Just in time production

Log-based Anomaly Detection based on EVT Theory with feedback

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Liu, Jinyang Huang, Junjie Huo, Yintong Jiang, Zhihan Gu, Jiazhen Chen, Zhuangbin Feng, Cong Yan, Minzhi Lyu, Michael R. The Chinese University of Hong Kong Hong Kong School of Software Engineering Sun Yat-sen University China Computing and Networking Innovation Lab Huawei Cloud Computing Technology Co. Ltd China HCC Lab Huawei Cloud Computing Technology Co. Ltd China The Chinese University of Hong Kong China

System logs play a critical role in maintaining the reliability of software systems. Fruitful studies have explored automatic log-based anomaly detection and achieved notable accuracy on benchmark datasets. However, when applied to large-scale cloud systems, these solutions face limitations due to high resource consumption and lack of adaptability to evolving logs. In this paper, we present an accurate, lightweight, and adaptive log-based anomaly detection framework, referred to as ScaleAD. Our method introduces a Trie-based Detection Agent (TDA) that employs a lightweight, dynamically-growing trie structure for real-time anomaly detection. To enhance TDA’s accuracy in response to evolving log data, we enable it to receive feedback from experts. Interestingly, our findings suggest that contemporary large language models, such as ChatGPT, can provide feedback with a level of consistency comparable to human experts, which can potentially reduce manual verification efforts. We extensively evaluate ScaleAD on two public datasets and an industrial dataset. The results show that ScaleAD outperforms all baseline methods in terms of effectiveness, runs 2× to 10× faster and only consumes 5% to 41% of the memory resource. © 2023, CC BY-NC-ND.

关键词： Anomaly detection

Practical Anomaly Detection over Multivariate Monitoring Metrics for Online Services

学校读者我要写书评

暂无评论

Practical Anomaly Detection over Multivariate Monitoring Met...

International Symposium on software Reliability engineering (ISSRE)

作者： Jinyang Liu Tianyi Yang Zhuangbin Chen Yuxin Su Cong Feng Zengyin Yang Michael R. Lyu The Chinese University of Hong China School of Software Engineering Sun Yat-sen University Zhuhai China Computing and Networking Innovation Lab Huawei Cloud Computing Technology Co. Ltd China

As modern software systems continue to grow in terms of complexity and volume, anomaly detection on multivariate monitoring metrics, which profile systems’ health status, becomes more and more critical and challenging. In particular, the dependency between different metrics and their historical patterns plays a critical role in pursuing prompt and accurate anomaly detection. Existing approaches fall short of industrial needs for being unable to capture such information efficiently. To fill this significant gap, in this paper, we propose CMAnomaly, an anomaly detection framework on multivariate monitoring metrics based on collaborative machine. The proposed collaborative machine is a mechanism to capture the pairwise interactions along with feature and temporal dimensions with linear time complexity. Cost-effective models can then be employed to leverage both the dependency between monitoring metrics and their historical patterns for anomaly detection. The proposed framework is extensively evaluated with both public data and industrial data collected from a large-scale online service system of huawei Cloud. The experimental results demonstrate that compared with state-of-the-art baseline models, CMAnomaly achieves an average F1 score of 0.9494, outperforming baselines by 6.77% ~ 10.68%, and runs 10× ~ 20× faster. Furthermore, we also share our experience of deploying CMAnomaly in huawei Cloud.

关键词：

Satellite Federated Fine-Tuning for Foundation Models in Space Computing Power Networks

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Zhu, Yan Zhu, Jingyang Wang, Ting Shi, Yuanming Jiang, Chunxiao Letaief, Khaled Ben MoE Engineering Research-Center of Software/Hardware Co-design Technology and Application The Shanghai Key Lab. of Trustworthy Computing East China Normal University Shanghai200062 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Advancements in artificial intelligence (AI) and low-earth orbit (LEO) satellites have promoted the application of large remote sensing foundation models for various downstream tasks. However, direct downloading of these models for fine-tuning on the ground is impeded by privacy concerns and limited bandwidth. Satellite federated learning (FL) offers a solution by enabling model fine-tuning directly on-board satellites and aggregating model updates without data downloading. Nevertheless, for large foundation models, the computational capacity of satellites is insufficient to support effective on-board fine-tuning in traditional satellite FL frameworks. To address these challenges, we propose a satellite-ground collaborative federated fine-tuning framework. The key of the framework lies in how to reasonably decompose and allocate model components to alleviate insufficient on-board computation capabilities. During fine-tuning, satellites exchange intermediate results with ground stations or other satellites for forward propagation and back propagation, which brings communication challenges due to the special communication topology of space transmission networks, such as intermittent satellite-ground communication, short duration of satellite-ground communication windows, and unstable inter-orbit inter-satellite links (ISLs). To reduce transmission delays, we further introduce tailored communication strategies that integrate both communication and computing resources. Specifically, we propose a parallel intra-orbit communication strategy, a topology-aware satellite-ground communication strategy, and a latency-minimalization inter-orbit communication strategy to reduce space communication costs. Simulation results demonstrate significant reductions in training time with improvements of approximately 33%. Copyright © 2025, The Authors. All rights reserved.

关键词： Satellite communication systems