2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)最新文献

Improving Adaptive Monitoring with Incremental Runtime Model Queries 使用增量运行时模型查询改进自适应监控

2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) Pub Date : 2021-05-01 DOI: 10.1109/SEAMS51251.2021.00019

Matthias Barkowsky, Thomas Brand, H. Giese

{"title":"Improving Adaptive Monitoring with Incremental Runtime Model Queries","authors":"Matthias Barkowsky, Thomas Brand, H. Giese","doi":"10.1109/SEAMS51251.2021.00019","DOIUrl":"https://doi.org/10.1109/SEAMS51251.2021.00019","url":null,"abstract":"Runtime models are often employed in different forms in self-adaptive software. They reflect, due to the causal connection, the current state of the adaptable software. Runtime model querying can be used to check whether the runtime model indicates the need for an adaptation or collect the information necessary to decide which adaptation should be performed. Given a set of runtime model queries, a natural question is how the effort to obtain and maintain the required information at runtime can be reduced. Besides the general need to reduce the overhead resulting from self-adaptation concerning its environmental impact, also restricted resources may make this a particularly relevant optimization. Two opportunities for effort reduction are the query evaluation and the necessary system state sensing. In this paper we consider both opportunities by investigating how our approach for adaptive monitoring with architecture runtime models can be improved through a better integration with an enhanced mechanism for incremental querying. We outline how incremental queries in this context can be optimized to better support adaptive monitoring. We compare different approach variants and present first very promising evaluation results that indicate that the optimized incremental queries have the potential to substantially reduce the monitoring effort and query time.","PeriodicalId":258262,"journal":{"name":"2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128458944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

“Know What You Know”: Predicting Behavior for Learning-Enabled Systems When Facing Uncertainty “知道你所知道的”:面对不确定性时预测学习系统的行为

2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) Pub Date : 2021-05-01 DOI: 10.1109/SEAMS51251.2021.00020

Michael Austin Langford, B. Cheng

{"title":"“Know What You Know”: Predicting Behavior for Learning-Enabled Systems When Facing Uncertainty","authors":"Michael Austin Langford, B. Cheng","doi":"10.1109/SEAMS51251.2021.00020","DOIUrl":"https://doi.org/10.1109/SEAMS51251.2021.00020","url":null,"abstract":"Since deep learning systems do not generalize well when training data is incomplete and missing coverage of corner cases, it is difficult to ensure the robustness of safety-critical self-adaptive systems with deep learning components. Stakeholders require a reasonable level of confidence that a safety-critical system will behave as expected in all contexts. However, uncertainty in the behavior of safety-critical Learning-Enabled Systems (LESs) arises when run-time contexts deviate from training and validation data. To this end, this paper proposes an approach to develop a more robust safety-critical LES by predicting its learned behavior when exposed to uncertainty and thereby enabling mitigating countermeasures for predicted failures. By combining evolutionary computation with machine learning, an automated method is introduced to assess and predict the behavior of an LES when faced with previously unseen environmental conditions. By experimenting with Deep Neural Networks (DNNs) under a variety of adverse environmental changes, the proposed method is compared to a Monte Carlo (i.e., random sampling) method. Results indicate that when Monte Carlo sampling fails to capture uncommon system behavior, the proposed method is better at training behavior models with fewer training examples required.","PeriodicalId":258262,"journal":{"name":"2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)","volume":"161 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129024213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Reliability Prediction of Self-Adaptive Systems Managing Uncertain AI Black-Box Components 管理不确定AI黑匣子组件的自适应系统可靠性预测

2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) Pub Date : 2021-05-01 DOI: 10.1109/SEAMS51251.2021.00024

Max Scheerer, Ralf H. Reussner

引用次数: 4

RDMSim: An Exemplar for Evaluation and Comparison of Decision-Making Techniques for Self-Adaptation RDMSim:自适应决策技术评价与比较的一个范例

2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) Pub Date : 2021-05-01 DOI: 10.1109/SEAMS51251.2021.00039

Huma Samin, L. H. Paucar, N. Bencomo, Cesar M. Carranza Hurtado, Erik M. Fredericks

{"title":"RDMSim: An Exemplar for Evaluation and Comparison of Decision-Making Techniques for Self-Adaptation","authors":"Huma Samin, L. H. Paucar, N. Bencomo, Cesar M. Carranza Hurtado, Erik M. Fredericks","doi":"10.1109/SEAMS51251.2021.00039","DOIUrl":"https://doi.org/10.1109/SEAMS51251.2021.00039","url":null,"abstract":"Decision-making for self-adaptation approaches need to address different challenges, including the quantification of the uncertainty of events that cannot be foreseen in advance and their effects, and dealing with conflicting objectives that inherently involve multi-objective decision making (e.g., avoiding costs vs. providing reliable service). To enable researchers to evaluate and compare decision-making techniques for self-adaptation, we present the RDMSim exemplar. RDMSim enables researchers to evaluate and compare techniques for decision-making under environmental uncertainty that support self-adaptation. The focus of the exemplar is on the domain problem related to Remote Data Mirroring, which gives opportunity to face the challenges described above. RDMSim provides probe and effector components for easy integration with external adaptation managers, which are associated with decision-making techniques and based on the MAPE-K loop. Specifically, the paper presents (i) RDMSim, a simulator for real-world experimentation, (ii) a set of realistic simulation scenarios that can be used for experimentation and comparison purposes, (iii) data for the sake of comparison.","PeriodicalId":258262,"journal":{"name":"2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)","volume":"320 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115869707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

If a System is Learning to Self-adapt, Who's Teaching? 如果一个系统在学习自我适应，谁在教学?

2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) Pub Date : 2021-05-01 DOI: 10.1109/SEAMS51251.2021.00043

Yehia El-khatib, Abdessalam Elhabbash

引用次数: 2

Platooning LEGOs: An Open Physical Exemplar for Engineering Self-Adaptive Cyber-Physical Systems-of-Systems 排队乐高：自适应网络物理系统工程的开放式物理范例

2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) Pub Date : 2021-05-01 DOI: 10.1109/SEAMS51251.2021.00038

Yong-Jun Shin, Lingjun Liu, Sang Hyun, Doo-Hwan Bae

{"title":"Platooning LEGOs: An Open Physical Exemplar for Engineering Self-Adaptive Cyber-Physical Systems-of-Systems","authors":"Yong-Jun Shin, Lingjun Liu, Sang Hyun, Doo-Hwan Bae","doi":"10.1109/SEAMS51251.2021.00038","DOIUrl":"https://doi.org/10.1109/SEAMS51251.2021.00038","url":null,"abstract":"Many modern systems interact with both cyber and physical environments. They are complex systems in which multiple constituent systems work together to achieve higher-level goals. These systems are called cyber-physical systems of systems (CPSoS). As the interest in CPSoS, such as platooning vehicles and robot-based smart factories, increases, engineering for adaptive goal achievement of CPSoS is needed. Common exemplars of a research community can facilitate research; however, existing exemplars of CPSoS are mostly based on virtual simulations. Although this allows researchers to share experimental scenarios and environments, it has the limitation that it is difficult to conduct experiments that reflect actual physical environments. To overcome this limitation, we propose a physical exemplar of an industrial CPSoS, called Platooning LEGOs, which employs platooning technology that is actively being developed by the autonomous driving industry. A platoon, in which independent vehicles drive together, achieves SoS-level goals through adaptive behavioral decisions of the vehicles. This exemplar provides a physical experimental environment that can be implemented with LEGOs. A simple LEGO assembly allows the use of real data from sensors and actuators, facilitating a focus on software engineering without considerable mechanical knowledge. Moreover, as this is an open exemplar, researchers can implement the same physical experimental environment with a limited budget and expand its physical or software elements. We provide system descriptions, physical and software implementation manuals, and sample experimental results of Platooning LEGOs.","PeriodicalId":258262,"journal":{"name":"2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126019677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

RoboMAX: Robotic Mission Adaptation eXemplars RoboMAX:机器人任务适应范例

2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) Pub Date : 2021-05-01 DOI: 10.1109/SEAMS51251.2021.00040

M. Askarpour, Christos Tsigkanos, C. Menghi, R. Calinescu, Patrizio Pelliccione, Sergio García, Ricardo Caldas, Tim J. von Oertzen, M. Wimmer, L. Berardinelli, M. Rossi, M. Bersani, Gabriel S. Rodrigues

引用次数: 11

The Hitchhiker's Guide to the End-of-Life for Smart Devices 《智能设备寿命终结漫游指南

2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) Pub Date : 2021-05-01 DOI: 10.1109/SEAMS51251.2021.00033

Sebastian Lawrenz, Benjamin Leiding

{"title":"The Hitchhiker's Guide to the End-of-Life for Smart Devices","authors":"Sebastian Lawrenz, Benjamin Leiding","doi":"10.1109/SEAMS51251.2021.00033","DOIUrl":"https://doi.org/10.1109/SEAMS51251.2021.00033","url":null,"abstract":"The progressing severe environmental pollution and dwindling nonrenewable resources force society to increase the reuse of electronic waste (e-waste) to ensure the availability of sufficient resources for future products and an environment worth living in. The constant growth and expansion of the Internet of Things (IoT) and related smart devices amplifies the aforementioned problem, especially since a large amount of e-waste is either not disposed of correctly or hoarded at home. Thus, a solution is required to ease and incentivize the correct disposal of e-waste. While the growing number of smart devices increases the amount of e-waste, they also offer new technical opportunities to solve the very same problem they create. Instead of relying on the smart device owner to correctly dispose them at the recycling center, smart devices could arrange their delivery to the nearby recycling center themselves in a self-organized manner once they reach their end-of-life or are not used any further by the owner. This work introduces the Hitchhiker service platform that addresses the given problem of smart device e-waste. We outline the ecosystem and its stakeholders by following the Design Science Research approach. Moreover, we introduce the Hitchhiker system architecture for a self-adaptive disposal system of smart devices and explain selected system engagement processes. Finally, we discuss extensions of the service platform to support non-smart legacy devices that are not yet capable of organizing their own disposal logistics.","PeriodicalId":258262,"journal":{"name":"2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)","volume":"246 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120862192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Threat modeling at run time: the case for reflective and adaptive threat management (NIER track) 运行时的威胁建模:反思和自适应威胁管理的案例(NIER轨道)

2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) Pub Date : 2021-05-01 DOI: 10.1109/SEAMS51251.2021.00034

D. Landuyt, L. Pasquale, Laurens Sion, W. Joosen

{"title":"Threat modeling at run time: the case for reflective and adaptive threat management (NIER track)","authors":"D. Landuyt, L. Pasquale, Laurens Sion, W. Joosen","doi":"10.1109/SEAMS51251.2021.00034","DOIUrl":"https://doi.org/10.1109/SEAMS51251.2021.00034","url":null,"abstract":"Threat modeling is an analysis activity aimed at eliciting viable and realistic security and privacy threats in the design of a software-intensive system. Threat modeling allows for a by-design approach, mitigating problems before they arise and avoiding later costly development efforts. However, it mainly pays off in software construction approaches that rely on planned architectures, in which sources of threats can be anticipated beforehand. These axiomatic assumptions are, however, increasingly untrue in contemporary software development practices in which software systems evolve drastically in later stages. In addition, software-intensive systems are increasingly faced with uncertainty in their operational contexts, and these are nearly impossible to enumerate in early development stages. In this article, we first present the idea of reflective threat modeling, which involves the automated derivation of architectural system models from run-time and operational system artifacts, providing the threat modeler with an accurate and workable run-time inspection view of the system. We then outline and motivate the potential of adopting threat analysis models as a basis for holistic and adaptive threat management through integration of adaptive security and privacy technologies. This will enable systems to autonomously respond to emerging threats by dynamically activating dedicated controls or via run-time reconfiguration.","PeriodicalId":258262,"journal":{"name":"2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127722811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Towards a Self-Adaptive Architecture for Federated Learning of Industrial Automation Systems 面向工业自动化系统联邦学习的自适应体系结构研究

2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) Pub Date : 2021-05-01 DOI: 10.1109/SEAMS51251.2021.00035

Nicola Franco, H. Van, Marc Dreiser, Gereon Weiss

{"title":"Towards a Self-Adaptive Architecture for Federated Learning of Industrial Automation Systems","authors":"Nicola Franco, H. Van, Marc Dreiser, Gereon Weiss","doi":"10.1109/SEAMS51251.2021.00035","DOIUrl":"https://doi.org/10.1109/SEAMS51251.2021.00035","url":null,"abstract":"Emerging Industry 4.0 architectures deploy data-driven applications and artificial intelligence services across multiple locations under varying ownership, and require specific data protection and privacy considerations to not expose confidential data to third parties. For this reason, federated learning provides a framework for optimizing machine learning models in single manufacturing facilities without requiring access to training data. In this paper, we propose a self-adaptive architecture for federated learning of industrial automation systems. Our approach considers the involved entities on the different levels of abstraction of an industrial ecosystem. To achieve the goal of global model optimization and reduction of communication cycles, each factory internally trains the model in a self-adaptive manner and sends it to the centralized cloud server for global aggregation. We model a multi-assignment optimization problem by dividing the dataset into a number of subsets equal to the number of devices. Each device chooses the right subset to optimize the model at each local iteration. Our initial analysis shows the convergence property of the algorithm on a training dataset with different numbers of factories and devices. Moreover, these results demonstrate higher model accuracy with our self-adaptive architecture than the federated averaging approach for the same number of communication cycles.","PeriodicalId":258262,"journal":{"name":"2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122817161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3