Variability Management in Self-Adaptive Systems through Deep Learning: A Dynamic Software Product Line Approach

Self-adaptive systems can autonomously adjust their behavior in response to environmental changes. Nowadays, not only can these systems be engineered individually, but they can also be conceived as members of a family based on the approach of dynamic software product lines. Through systematic mappin...

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Bibliographic Details
Published in:Electronics (Basel) 2024-03, Vol.13 (5), p.905
Main Authors: Aguayo, Oscar, Sepúlveda, Samuel, Mazo, Raúl
Format: Article
Language:English
Subjects:
Adaptability
Adaptation
Adaptive control
Adaptive systems
Air quality
Algorithms
Artificial intelligence
Artificial neural networks
Automobile industry
Deep learning
Distance learning
Machine learning
Methods
Neural networks
Permutations
Product development
Product lines
Recommender systems
Run time (computers)
Self adaptive control systems
Software development
Software engineering
Variability
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Summary:Self-adaptive systems can autonomously adjust their behavior in response to environmental changes. Nowadays, not only can these systems be engineered individually, but they can also be conceived as members of a family based on the approach of dynamic software product lines. Through systematic mapping, we build on the identified gaps in the variability management of self-adaptive systems; we propose a framework that improves the adaptive capability of self-adaptive systems through feature model generation, variation point generation, the selection of a variation point, and runtime variability management using deep learning and the monitor–analysis–plan–execute–knowledge (MAPE-K) control loop. We compute the permutation of domain features and obtain all the possible variation points that a feature model can possess. After identifying variation points, we obtain an adaptation rule for each variation point of the corresponding product line through a two-stage training of an artificial neural network. To evaluate our proposal, we developed a test case in the context of an air quality-based activity recommender system, in which we generated 11 features and 32 possible variations. The results obtained with the proof of concept show that it is possible to manage identifying new variation points at runtime using deep learning. Future research will employ generating and building variation points using artificial intelligence techniques.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13050905