{"title":"Pretrain, Prompt, and Transfer: Evolving Digital Twins for Time-to-Event Analysis in Cyber-Physical Systems","authors":"Qinghua Xu;Tao Yue;Shaukat Ali;Maite Arratibel","doi":"10.1109/TSE.2024.3388572","DOIUrl":null,"url":null,"abstract":"Cyber-physicalnd systems (CPSs), e.g., elevators and autonomous driving systems, are progressively permeating our everyday lives. To ensure their safety, various analyses need to be conducted, such as anomaly detection and time-to-event analysis (the focus of this paper). Recently, it has been widely accepted that digital Twins (DTs) can be an efficient method to aid in developing, maintaining, and safe and secure operation of CPSs. However, CPSs frequently evolve, e.g., with new or updated functionalities, which demand their corresponding DTs be co-evolved, i.e., in synchronization with the CPSs. To that end, we propose a novel method, named \n<monospace>PPT</monospace>\n, utilizing an uncertainty-aware transfer learning for DT evolution. Specifically, we first pretrain \n<monospace>PPT</monospace>\n with a pretraining dataset to acquire generic knowledge about the CPSs, followed by adapting it to a specific CPS with the help of prompt tuning. Results highlight that \n<monospace>PPT</monospace>\n is effective in time-to-event analysis in both elevator and autonomous driving case studies, on average, outperforming a baseline method by 7.31 and 12.58 in terms of Huber loss, respectively. The experiment results also affirm the effectiveness of transfer learning, prompt tuning, and uncertainty quantification in terms of reducing Huber loss by at least 21.32, 3.14, and 4.08, respectively, in both case studies.","PeriodicalId":13324,"journal":{"name":"IEEE Transactions on Software Engineering","volume":null,"pages":null},"PeriodicalIF":6.5000,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Software Engineering","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10500740/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
Cyber-physicalnd systems (CPSs), e.g., elevators and autonomous driving systems, are progressively permeating our everyday lives. To ensure their safety, various analyses need to be conducted, such as anomaly detection and time-to-event analysis (the focus of this paper). Recently, it has been widely accepted that digital Twins (DTs) can be an efficient method to aid in developing, maintaining, and safe and secure operation of CPSs. However, CPSs frequently evolve, e.g., with new or updated functionalities, which demand their corresponding DTs be co-evolved, i.e., in synchronization with the CPSs. To that end, we propose a novel method, named
PPT
, utilizing an uncertainty-aware transfer learning for DT evolution. Specifically, we first pretrain
PPT
with a pretraining dataset to acquire generic knowledge about the CPSs, followed by adapting it to a specific CPS with the help of prompt tuning. Results highlight that
PPT
is effective in time-to-event analysis in both elevator and autonomous driving case studies, on average, outperforming a baseline method by 7.31 and 12.58 in terms of Huber loss, respectively. The experiment results also affirm the effectiveness of transfer learning, prompt tuning, and uncertainty quantification in terms of reducing Huber loss by at least 21.32, 3.14, and 4.08, respectively, in both case studies.
期刊介绍:
IEEE Transactions on Software Engineering seeks contributions comprising well-defined theoretical results and empirical studies with potential impacts on software construction, analysis, or management. The scope of this Transactions extends from fundamental mechanisms to the development of principles and their application in specific environments. Specific topic areas include:
a) Development and maintenance methods and models: Techniques and principles for specifying, designing, and implementing software systems, encompassing notations and process models.
b) Assessment methods: Software tests, validation, reliability models, test and diagnosis procedures, software redundancy, design for error control, and measurements and evaluation of process and product aspects.
c) Software project management: Productivity factors, cost models, schedule and organizational issues, and standards.
d) Tools and environments: Specific tools, integrated tool environments, associated architectures, databases, and parallel and distributed processing issues.
e) System issues: Hardware-software trade-offs.
f) State-of-the-art surveys: Syntheses and comprehensive reviews of the historical development within specific areas of interest.