VariaMos

Mazo, Raúl; Muñoz-Fernández, Juan C.; Rincón, Luisa; Salinesi, Camille; Tamura, Gabriel

doi:10.1145/2791060.2791103

Cited by 22 publications

(6 citation statements)

References 13 publications

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“…VariaMos is a web-based tool that utilizes microservices to enable the specification, multi-language modeling, and multi-solver reasoning of SPL and DSPL projects [28]. In addition, it enables users to create their own engineering languages in a straightforward manner.…”

Section: Related Workmentioning

confidence: 99%

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

Aguayo,

Sepúlveda,

Mazo

2024

Electronics

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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.

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Section: Related Workmentioning

confidence: 99%

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

Aguayo,

Sepúlveda,

Mazo

2024

Electronics

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“…Since the algorithm determines the optimal or correct configuration, features can change their values to valid states once the constraints and dependencies have been satisfied. There are several tools for feature modeling, but in the literature only three for dynamic feature modeling have been identified: MOSKitt4SPL [23], VariaMos [24], and DyMMer [25]. However, only DyMMer was available.…”

Section: Dynamic Variabilitymentioning

confidence: 99%

“…Thus, some studies have extended the feature model to meet these challenges of representing dynamic variability [30][31][32]. However, few tools are supported for representing these extensions, such as the Variamos [24] and the DyMMer [25] tools. In our study, we used the DyMMer tool because it is available and have the option of assessing the quality of dynamic aspects using specific metrics.…”

Section: General Analysis Of the Solar Educational Ecosystemmentioning

confidence: 99%

A study on dynamic aspects variability in the SOLAR educational software ecosystem

Coutinho

Bezerra

2020

J Braz Comput Soc

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A Software Ecosystem (SECO) refers to a collection of software products with some degree of symbiotic relationship. SOLAR is a Virtual Learning Environment (VLE) that enables the publication of courses and interaction with them among its various users. In this context, SOLAR SECO emerges, where diverse situations of software evolution and maintenance are part of its development process. The aim of this paper is to discuss the dynamic variability of SOLAR educational software ecosystem and software modeling. As an example, dynamic variability aspects of the feature model of SOLAR VLE discussion forum functionality were discussed, one of the most widely used services within SOLAR SECO. As a major conclusion of this work, we identified that the use of the contextual feature diagram allows the study of the dynamic aspects of a system, even more supported by tools to support automatic measurement collection.

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“…First, we identified conceptual problems in the generalized application of the language to different case studies and examples. Second, we analyzed these issues during the implementation of a tool, VariaMos [24] 2 , to support modeling using REFAS. The following are the most important improvements in the revised meta-model: i. Simplification of the definition of conditional expressions, claims, and soft dependencies, without losing the semantics already supported.…”

Section: Meta-modelmentioning

confidence: 99%

“…In-depth analyses support the second challenge, with aggregation capabilities over concerns and its implied variables. To demonstrate our approach, we apply the modeling process to a running example using the VariaMos software tool 1 [24]. This tool implements the REFAS meta-model, its five default views, and its process.…”

Section: Introductionmentioning

confidence: 99%

Towards a Requirements Specification Multi-View Framework for Self-Adaptive Systems

Muñoz-Fernández

Tamura

Mazo

et al. 2015

CLEIej

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The analysis of self-adaptive systems (SAS) requirements involves addressing uncertainty from several sources. Despite advances in requirements for SAS, uncertainty remains an extremely difficult challenge. In this paper, we propose REFAS, a framework to model the requirements of self-adaptive software systems. Our aim with REFAS is to address and reduce uncertainty and to provide a language with sufficient power of expression to specify the different aspects of self-adaptive systems, relative to functional and non-functional requirements. The REFAS modeling language includes concepts closely related to these kind of requirements and their fulfillment, such as context variables, claims, and soft dependencies. Specifically, the paper´s contribution is twofold. First, REFAS supports different viewpoints and concerns related to requirements modeling, with key associations between them. Moreover, the modeler can define additional models and views by exploiting the REFAS meta-modeling capability, in order to capture additional aspects contributing to reduce uncertainty. Second, REFAS promotes in-depth analysis of all of the modeled concerns with aggregation and association capabilities, especially with context variables. Furthermore, we also define a process that enforces modeling requirements, considering different aspects of uncertainty. We demonstrate the applicability of REFAS by using the VariaMos software tool, which implements the REFAS meta-model, views, and process.

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VariaMos

Cited by 22 publications

References 13 publications

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

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

A study on dynamic aspects variability in the SOLAR educational software ecosystem

Towards a Requirements Specification Multi-View Framework for Self-Adaptive Systems

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