Achieving Knowledge Evolution in Dynamic Software Product Lines

Arcega, Lorena; Font, Jaime; Haugen, Øystein; Cetina, Carlos

doi:10.1109/saner.2016.24

Cited by 6 publications

(5 citation statements)

References 25 publications

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“…A check mark indicates whether the approach proposes solutions or deals with the different criteria. Arcega et al 57 Capilla et al 58 Czarnecki and Pietroszek 59 Deng et al 60 Elsner et al 61 Font et al 39 Gamez and Fuentes 54,62 Guo et al 9 Helleboogh et al 63 Mauro et al 64 Mende et al 65 Murta et al 66 Neves et al 12,8 Passos et al 7 Quinton et al 10 Seidl et al 3,67 Thüm et al 11 Dynamic software product lines. As modern systems demand more and more post-deployment activities and runtime capabilities, several DSPL approaches have emerged in the last decade to deal with these requirements 26 .…”

Section: Discussion and Threats To Validitymentioning

confidence: 99%

“…Such rules are very useful to support DSPL evolution and could be realized in our context via our update rules concept. Arcega et al 57 propose evolution strategies to migrate from the current version of a configuration space to an evolved one in a DSPL. Their approach thus enables the current running configuration to match the new configuration space.…”

Section: Related Workmentioning

confidence: 99%

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Evolution in dynamic software product lines

Quinton

Vierhauser

Rabiser

et al. 2020

J Software Evolu Process

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Many software systems today provide support for adaptation and reconfiguration at runtime, in response to changes in their environment. Such adaptive systems are designed to run continuously and may not be shut down for reconfiguration or maintenance tasks. The variability of such systems has to be explicitly managed, together with mechanisms that control their runtime adaptation and reconfiguration. Dynamic software product lines (DSPLs) can help to achieve this. However, dealing with evolution is particularly challenging in a DSPL, as changes made at runtime can easily lead to inconsistencies. This paper describes the challenges of evolving DSPLs using an example cyber-physical system for home automation. We discuss the shortcomings of existing work and present a reference architecture to support DSPL evolution. To demonstrate its feasibility and flexibility, we implemented the proposed reference architecture for two different DSPLs: the aforementioned cyber-physical system, which uses feature models to describe its variability, and a runtime monitoring infrastructure, which is based on decision models. To assess the industrial applicability of our approach, we also implemented the reference architecture for a real-world DSPL, an automation software system for injection molding machines. Our results provide evidence on the flexibility, performance and industrial applicability of our approach.

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Section: Discussion and Threats To Validitymentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Evolution in dynamic software product lines

Quinton

Vierhauser

Rabiser

et al. 2020

J Software Evolu Process

View full text Add to dashboard Cite

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“…Early approaches supporting basic runtime variability mechanisms for supporting automation and reconfiguration tasks of systems can be found in [17] [18]. In addition, variability transformations and the activation/deactivation of features during system execution are described for the case of smart home systems and aimed at handling different product reconfiguration [19] [20] [21]. Other experiences in the area of Wireless Sensor Networks use the FamiWare approach [22] to support runtime variability for reconfiguring variability models dynamically.…”

Section: A Runtime Variability Approachesmentioning

confidence: 99%

RuVa: A Runtime Software Variability Algorithm

et al. 2022

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Context-aware and smart systems that require runtime reconfiguration to cope with changes in the environment increasingly demand variability management mechanisms that can address runtime concerns. In recent years, we have witnessed new dynamic variability solutions using dynamic software product line (DSPL) approaches. However, while few solutions proposed so far have addressed the need to add, change and remove variants dynamically, none of them provide a way to check the constraints between features at runtime. Because all SAT solvers perform variability constraint checking in off-line mode, we suggest in this ongoing research paper the integration of RuVa, a runtime variability algorithm, with the FaMa tool suite to check feature constraints dynamically before a new feature is added or an existing feature is removed. This research suggests a novel approach to modifying the variability model of context-aware systems dynamically and check the feature constraints on the fly. We integrate our solution with a SAT solver that can be invoked at runtime by a cyber-physical system. We validate the effectiveness and performance of the proposed algorithm using simulations. We also provide a proof-of-concept for updating the configuration of a robot's variability model based on contextual changes.

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“…On the one hand, this means we are not concerned with the technicalities of how to switch between the feature combinations in the running system. This is the scope of other work, such as [72], [73], which thus may serve to address this concern. On the other hand, we do not take into account stateful or sequential constraints on adaptation itself.…”

Section: Limitationsmentioning

confidence: 99%

Feature-Model-Guided Online Learning for Self-Adaptive Systems

Metzger,

Quinton,

Mann

et al. 2019

Preprint

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A self-adaptive system can modify its own structure and behavior at runtime based on its perception of the environment, of itself and of its requirements. To develop a self-adaptive system, software developers codify knowledge about the system and its environment, as well as how adaptation actions impact on the system. However, the codified knowledge may be insufficient due to design time uncertainty, and thus a self-adaptive system may execute adaptation actions that do not have the desired effect. Online learning is an emerging approach to address design time uncertainty by employing machine learning at runtime. Online learning accumulates knowledge at runtime by, for instance, exploring not-yet executed adaptation actions. We address two specific problems with respect to online learning for self-adaptive systems. First, the number of possible adaptation actions can be very large. Existing online learning techniques randomly explore the possible adaptation actions, but this can lead to slow convergence of the learning process. Second, the possible adaptation actions can change as a result of system evolution. Existing online learning techniques are unaware of these changes and thus do not explore new adaptation actions, but explore adaptation actions that are no longer valid. We propose using feature models to give structure to the set of adaptation actions and thereby guide the exploration process during online learning. Experimental results involving four real-world systems suggest that considering the hierarchical structure of feature models may speed up convergence by 7.2% on average. Considering the differences between feature models before and after an evolution step may speed up convergence by 64.6% on average. For a cloud management system, experimental results suggest that this faster convergence leads to energy savings of 12.0% on average and a reduction in virtual machine migrations by 74.3% on average.

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Achieving Knowledge Evolution in Dynamic Software Product Lines

Cited by 6 publications

References 25 publications

Evolution in dynamic software product lines

Evolution in dynamic software product lines

RuVa: A Runtime Software Variability Algorithm

Feature-Model-Guided Online Learning for Self-Adaptive Systems

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