Extending Dynamic Software Product Lines with Temporal Constraints

Sousa, Gustavo; Rudametkin, Walter; Duchien, Laurence

doi:10.1109/seams.2017.6

Cited by 18 publications

(10 citation statements)

References 35 publications

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“…To go further, the feature models that are currently supported in our work cannot fully represent cloud-like platforms. These require taking into account complex attributes and constraints, such as time constraints and temporal constraints [24].…”

Section: Discussionmentioning

confidence: 99%

Safe Dynamic Reconfiguration of Concurrent Component-based Applications

Farhat

Bliudze

Duchien

2022

2022 IEEE 19th International Conference on Software Architecture Companion (ICSA-C)

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Cloud computing and cyber-physical systems involve software capable of adapting at run time to remain compliant with user demands and environmental constraints. This calls for extending the life cycle of software systems with a reconfiguration step to go beyond analysis, design, development and deployment. Existing approaches compute a new valid configuration at design time, at run time, or both, inducing computational or validation overheads for each reconfiguration step. We propose an approach that relies on variability models to acquire a representation of the set of valid configurations of a system. We use feature models to automatically generate a JavaBIP run-time variability model. The generated model monitors and controls the application behaviour by intercepting reconfiguration requests and executing them in such a manner as to ensure that all reachable configurations are valid without the need of pre-computing the possible configurations neither at design time nor at run-time while only inducing a minimal run-time computational overhead.

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Section: Discussionmentioning

confidence: 99%

Safe Dynamic Reconfiguration of Concurrent Component-based Applications

Farhat

Bliudze

Duchien

2022

2022 IEEE 19th International Conference on Software Architecture Companion (ICSA-C)

Self Cite

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“…Fifty-nine studies (42%) used descriptive statistics to show or summarize data in a meaningful way (e.g., using tables, graphs and charts), which allows identifying patterns that might emerge from the data. As an example, Sousa et al [64] analyze their experiment by calculating statistics (average, standard deviation, maximum, minimum) of execution times over 12 runs. Finally, 19 studies (14%) used statistical tests to analyze the data of the experiment and draw conclusions.…”

Section: E How Is the Experiments Data Analyzed?mentioning

confidence: 99%

“…Seventeen of these studies (55% of the 31 studies that discuss validity threats) provide an informal discussion of validity threats without referring to any particular types of threats. As an example, Sousa et al [64] discuss the limitations of their experiments in a separate section in an informal way. The most reported validity threats are internal and external validity, both discussed in 14 studies (45% of the 31 studies that discuss validity threats).…”

Section: E How Is the Experiments Data Analyzed?mentioning

confidence: 99%

How do we Evaluate Self-adaptive Software Systems? [replication package]

Gerostathopoulos,

Vogel,

Weyns

et al. 2021

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With the increase of research in self-adaptive systems, there is a need to better understand the way research contributions are evaluated. Such insights will support researchers to better compare new findings when developing new knowledge for the community. However, so far there is no clear overview of how evaluations are performed in self-adaptive systems. To address this gap, we conduct a mapping study. The study focuses on experimental evaluations published in the last decade at the prime venue of research in software engineering for self-adaptive systems-the International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). Results point out that specifics of self-adaptive systems require special attention in the experimental process, including the distinction of the managing system (i.e., the target of evaluation) and the managed system, the presence of uncertainties that affect the system behavior and hence need to be taken into account in data analysis, and the potential of managed systems to be reused across experiments, beyond replications. To conclude, we offer a set of suggestions derived from our study that can be used as input to enhance future experiments in self-adaptive systems.

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“…However, in reality, only certain paths may be permissible to reach another feature combination from the current one. To consider adaptation paths, our strategies could be enhanced by building on work such as [74], [75].…”

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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Extending Dynamic Software Product Lines with Temporal Constraints

Cited by 18 publications

References 35 publications

Safe Dynamic Reconfiguration of Concurrent Component-based Applications

Safe Dynamic Reconfiguration of Concurrent Component-based Applications

How do we Evaluate Self-adaptive Software Systems? [replication package]

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

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