| 1. |
- Bures, Tomas, et al.
(författare)
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Software Engineering for Smart Cyber-Physical Systems - Towards a Research Agenda : Report on the First International Workshop on Software Engineering for Smart CPS
- 2015
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Ingår i: Software Engineering Notes. - : ACM Press. - 0163-5948 .- 1943-5843. ; 40:6, s. 28-32
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Cyber-Physical Systems (CPS) are large interconnected softwareintensivesystems that influence, by sensing and actuating, thephysical world. Examples are traffic management and power grids.One of the trends we observe is the need to endow such systemswith the “smart” capabilities, typically in the form of selfawarenessand self-adaptation, along with the traditional qualitiesof safety and dependability. These requirements combined withspecifics of the domain of smart CPS – such as large scale, the roleof end-users, uncertainty, and open-endedness – render traditionalsoftware engineering (SE) techniques not directly applicable; makingsystematic SE of smart CPS a challenging task. This paperreports on the results of the First International Workshop on SoftwareEngineering of Smart Cyber-Physical Systems (SEsCPS2015), where participants discussed characteristics, challenges andopportunities of SE for smart CPS, with the aim to outline anagenda for future research in this important area.
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| 2. |
- Diamantopoulos, Nikos, et al.
(författare)
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Engineering for a science-centric experimentation platform
- 2020
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Ingår i: Proceedings - International Conference on Software Engineering. - New York, NY, USA : ACM. - 0270-5257. ; , s. 191-200
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Konferensbidrag (refereegranskat)abstract
- Netflix is an internet entertainment service that routinely employs experimentation to guide strategy around product innovations. As Netflix grew, it had the opportunity to explore increasingly specialized improvements to its service, which generated demand for deeper analyses supported by richer metrics and powered by more diverse statistical methodologies. To facilitate this, and more fully harness the skill sets of both engineering and data science, Netflix engineers created a science-centric experimentation platform that leverages the expertise of scientists from a wide range of backgrounds working on data science tasks by allowing them to make direct code contributions in the languages used by them (Python and R). Moreover, the same code that runs in production is able to be run locally, making it straightforward to explore and graduate both metrics and causal inference methodologies directly into production services. In this paper, we provide two main contributions. Firstly, we report on the architecture of this platform, with a special emphasis on its novel aspects: how it supports science-centric end-to-end workflows without compromising engineering requirements. Secondly, we describe its approach to causal inference, which leverages the potential outcomes conceptual framework to provide a unified abstarction layer for arbitrary statistical models and methodologies.
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| 3. |
- Filieri, Antonio, et al.
(författare)
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Control Strategies for Self-Adaptive Software Systems
- 2017
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Ingår i: ACM Transactions on Autonomous and Adaptive Systems. - : Association for Computing Machinery (ACM). - 1556-4665 .- 1556-4703. ; 11:4
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Tidskriftsartikel (refereegranskat)abstract
- The pervasiveness and growing complexity of software systems are challenging software engineering to design systems that can adapt their behavior to withstand unpredictable, uncertain, and continuously changing execution environments. Control theoretical adaptation mechanisms have received growing interest from the software engineering community in the last few years for their mathematical grounding, allowing formal guarantees on the behavior of the controlled systems. However, most of these mechanisms are tailored to specific applications and can hardly be generalized into broadly applicable software design and development processes.This article discusses a reference control design process, from goal identification to the verification and validation of the controlled system. A taxonomy of the main control strategies is introduced, analyzing their applicability to software adaptation for both functional and nonfunctional goals. A brief extract on how to deal with uncertainty complements the discussion. Finally, the article highlights a set of open challenges, both for the software engineering and the control theory research communities.
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| 4. |
- Filieri, Antonio, et al.
(författare)
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Software Engineering Meets Control Theory
- 2015
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Ingår i: 2015 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems. - Piscataway, NJ, USA : IEEE Press. - 9780769555676 ; , s. 71-82
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Konferensbidrag (refereegranskat)abstract
- The software engineering community has proposed numerous approaches for making software self-adaptive. These approaches take inspiration from machine learning and control theory, constructing software that monitors and modifies its own behavior to meet goals. Control theory, in particular, has received considerable attention as it represents a general methodology for creating adaptive systems. Control-theoretical software implementations, however, tend to be ad hoc. While such solutions often work in practice, it is difficult to understand and reason about the desired properties and behavior of the resulting adaptive software and its controller. This paper discusses a control design process for software systems which enables automatic analysis and synthesis of a controller that is guaranteed to have the desired properties and behavior. The paper documents the process and illustrates its use in an example that walks through all necessary steps for self-adaptive controller synthesis.
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| 5. |
- Gerostathopoulos, Ilias, et al.
(författare)
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Architectural Homeostasis in Self-Adaptive Software-Intensive Cyber-Physical Systems
- 2016
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. - 9783319489919 ; 9839 LNCS, s. 113-128
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Konferensbidrag (refereegranskat)abstract
- Self-adaptive software-intensive cyber-physical systems (sasiCPS) en-counter a high level of run-time uncertainty. State-of-the-art architecture-based self-adaptation approaches assume designing against a fixed set of situations that warrant self-adaptation; as a result, failures may appear when sasiCPS operate in environment conditions they are not specifically designed for. In response, we propose to increase the homeostasis of sasiCPS, i.e., the capacity to maintain an operational state despite run-time uncertainty, by introducing run-time changes to the architecture-based self-adaptation strategies according to environment stimuli. In addition to articulating the main idea of architectural homeostasis, we describe three mechanisms that reify the idea: (i) collaborative sensing, (ii) faulty component isolation from adaptation, and (iii) enhancing mode switching. More-over, our experimental evaluation of the three mechanisms confirms that allowing a complex system to change its self-adaptation strategies helps the system recover from runtime errors and abnormalities and keep it in an operational state.
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| 6. |
- Gerostathopoulos, Ilias, et al.
(författare)
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Continuous Data-driven Software Engineering : Towards a Research Agenda
- 2019
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Ingår i: Software Engineering Notes. - : Association for Computing Machinery (ACM). - 0163-5948 .- 1943-5843. ; 44:3, s. 60-64
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Tidskriftsartikel (refereegranskat)abstract
- The rapid pace with which software needs to be built, together with the increasing need to evaluate changes for end users both quantitatively and qualitatively calls for novel software engineering approaches that focus on short release cycles, continuous deployment and delivery, experiment-driven feature development, feedback from users, and rapid tool-assisted feedback to developers. To realize these approaches there is a need for research and innovation with respect to automation and tooling, and furthermore for research into the organizational changes that support flexible data-driven decision-making in the development lifecycle. Most importantly, deep synergies are needed between software engineers, managers, and data scientists. This paper reports on the results of the joint 5th International Workshop on Rapid Continuous Software Engineering (RCoSE 2019) and the 1st International Workshop on Data-Driven Decisions, Experimentation and Evolution (DDrEE 2019), which focuses on the challenges and potential solutions in the area of continuous data-driven software engineering.
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| 7. |
- Gerostathopoulos, Ilias, et al.
(författare)
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Message from the RCoSE-DDrEE 2019 workshop organizers
- 2019
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
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| 8. |
- Gerostathopoulos, Ilias, et al.
(författare)
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Tuning self-adaptation in cyber-physical systems through architectural homeostasis
- 2019
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Ingår i: Journal of Systems and Software. - : Elsevier BV. - 0164-1212. ; 148, s. 37-55
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Tidskriftsartikel (refereegranskat)abstract
- Self-adaptive software-intensive cyber-physical systems (sasiCPS) encounter a high level of run-time uncertainty. State-of-the-art architecture-based self-adaptation approaches assume designing against a fixed set of situations that warrant self-adaptation. As a result, failures may appear when sasiCPS operate in environment conditions they are not specifically designed for. In response, we propose to increase the homeostasis of sasiCPS, i.e., the capacity to maintain an operational state despite run-time uncertainty, by introducing run-time changes to the architecture-based self-adaptation strategies according to environment stimuli. In addition to articulating the main idea of architectural homeostasis, we introduce four mechanisms that reify the idea: (i) collaborative sensing, (ii) faulty component isolation from adaptation, (iii) enhancing mode switching, and (iv) adjusting guards in mode switching. Moreover, our experimental evaluation of the four mechanisms in two different case studies confirms that allowing a complex system to change its self-adaptation strategies helps the system recover from run-time errors and abnormalities and keep it in an operational state.
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| 9. |
- Götz, Sebastian, et al.
(författare)
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Adaptive exchange of distributed partial models@run.time for highly dynamic systems
- 2015
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Ingår i: 2015 IEEE/ACM 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). ; , s. 64-70
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Konferensbidrag (refereegranskat)abstract
- Future software systems will be highly dynamic. We are already experiencing, for example, a world where Cyber-Physical Systems (CPSs) play a more and more crucial role. CPSs integrate computational, physical, and networking elements; they comprise a number of subsystems, or entities, that are connected and work together. The open and highly distributed nature of the resulting system gives rise to unanticipated runtime management issues such as the organization of subsystems and resource optimization. In this paper, we focus on the problem of knowledge sharing among cooperating entities of a highly distributed and self-adaptive CPS. Specifically, the research question we address is how to minimize the knowledge that needs to be shared among the entities of a CPS. If all entities share all their knowledge with each other, the performance, energy and memory consumption as well as privacy are unnecessarily negatively impacted. To reduce the amount of knowledge to share between CPS entities, we envision a role-based adaptive knowledge exchange technique working on partial runtime models, i.e., models reflecting only part of the state of the CPS. Our approach supports two adaptation dimensions: the runtime type of knowledge and conditions over the knowledge. We illustrate the feasibility of our technique by discussing its realization based on two state-of-the-art approaches.
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| 10. |
- Therodouou, Vaslieios, 1980, et al.
(författare)
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Theta Architecture: Preserving the Quality of Analytics in Data-Driven Systems
- 2017
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Ingår i: Workshop on Novel Techniques for Integrating Big Data (BigNovelTI 2017).
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Konferensbidrag (refereegranskat)abstract
- With the recent advances in Big Data storage and processing, there is a real potential of data-driven software systems, i.e., systems that employ analysis of large amounts of data to inform their runtime decisions. However, for these decisions to be trustworthy and dependable, one needs to deal with the well-known challenges on the data analysis domain: data scarcity, low-quality of data available for analysis, low veracity of data and subsequent analysis results, data privacy constraints that hinder the analysis. A promising solution is to introduce exibility in the data analytics part of the system enabling optimization at runtime of the algorithms and data streams based on the combination of veracity, privacy and scarcity in order to preserve the target level of quality of the data-driven decisions. In this paper, we investigate this solution by providing an adaptive reference architecture and illustrate its applicability with an example from the trac management domain.
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