4.6 Article

RuVa: A Runtime Software Variability Algorithm

Journal

IEEE ACCESS
Volume 10, Issue -, Pages 52525-52536

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2022.3175505

Keywords

Runtime; Robots; Adaptation models; Software; Context modeling; Behavioral sciences; Vehicle dynamics; Software variability; runtime variability; dynamic software product lines; feature model; context features; reconfiguration; robots

Ask authors/readers for more resources

This research paper proposes a method to integrate the RuVa algorithm with the FaMa tool suite to dynamically check feature constraints and modify the variability model of context-aware systems.
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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available