Context-aware reconfiguration in evolving software product lines

Mauro, Jacopo; Nieke, Michael; Seidl, Christoph; Yu, Ingrid Chieh

doi:10.1016/j.scico.2018.05.002

Cited by 19 publications

(32 citation statements)

References 36 publications

(78 reference statements)

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“…• Mauro et al [18] present a framework for the modeling and evolving reconfiguration of context-aware SPL instances. They consider the environmental impact on updated features and new contextual information on the SPL evolution.…”

Section: Related Workmentioning

confidence: 99%

Reviewing Diagnosis Solutions for Valid Product Configurations in the Automated Analysis of Feature Models

Vidal-Silva¹,

Rubio²,

Madariaga³

et al. 2019

ijacsa

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A Feature Model (FM) is an information model to represent commonalities and variabilities for all the products of a Software Product Line (SPL). The complexity and big size of real feature models makes their manual analysis for determining the product configurations validity a tedious or even infeasible task. Efficient solutions for the diagnosis of errors in the Automated Analysis of Feature Models (AAFM) already exist such as FMDiag and FlexDiag. Thus, this work describes the fundamental basis for both diagnosis algorithms to apply the first of them on the validity of FM product configurations. The results highlight the applicability and efficiency of FMDiag and invite us to look for additional applications of that algorithm in the AAFM scenarios.

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Section: Related Workmentioning

confidence: 99%

Reviewing Diagnosis Solutions for Valid Product Configurations in the Automated Analysis of Feature Models

Vidal-Silva¹,

Rubio²,

Madariaga³

et al. 2019

ijacsa

View full text Add to dashboard Cite

show abstract

“…In this section, we formalize the notion of Context-aware Feature Model (CaFM). Following [24], a context can be considered as a variable that someone externally (e.g., the user of the software or the environment) can set. These new variables impose constraints over features and therefore on the products that can be obtained.…”

Section: Context-aware Feature Modelsmentioning

confidence: 99%

“…To the best of our knowledge, this is the first paper to formally define CaFMs and their anomaly analysis. CaFMs were originally introduced in [24] and the idea that QBF solvers can be used to check the voidness was first presented in [23] without providing, however, any comparison w.r.t. the iterative approach.…”

Section: Related Work and Conclusionmentioning

confidence: 99%

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Anomaly Detection and Explanation in Context-Aware Software Product Lines

Mauro

Nieke

Seidl

et al. 2017

Proceedings of the 21st International Systems and Software Product Line Conference - Volume B

Self Cite

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Feature Models are a mechanism to organize the configuration space and facilitate the construction of software variants by describing configuration options using features, i.e., a name representing a functionality. The development of Feature Models is an error prone activity and detecting their anomalies is a challenging and important task needed to promote their usage. Recently, Feature Models have been extended with context to capture the correlation of configuration options with contextual influences and user customizations. Unfortunately, this extension makes the task of detecting anomalies harder. In this paper, we formalize the anomaly analysis in Context-aware Feature Models and we show how Quantified Boolean Formula (QBF) solvers can be used to detect anomalies without relying on iterative calls to a SAT solver. By extending the reconfigurator engine HyVarRec, we present findings evidencing that QBF solvers can outperform the common techniques for anomaly analysis.

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“…Although the identified issues are motivated by a press line domain, we notice that the ICPS evolution is something known in the literature [13,14], and considering the domain analysis performed in our previous work [5], we realized that ICPS evolution besides the different ICPS topologies are not specific problems for press lines. In the literature, many authors consider software evolution [14][15][16][17], but very few of them consider software and hardware evolution [18,19] even though those who consider runtime variability do not consider uncertainties; hence, as far as we know, no one in the literature has give the response to detecting ICPS evolution in order (1) to have the traceability of what has happened in an ICPS over time; (2) to classify the occurred evolution in order to communicate immediately to the users to avoid any bad decisions; and (3) to give a solution which considers different ICPS topologies.…”

Section: Introductionmentioning

confidence: 99%

Industrial Cyber-Physical System Evolution Detection and Alert Generation

2019

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Industrial Cyber-Physical System (ICPS) monitoring is increasingly being used to make decisions that impact the operation of the industry. Industrial manufacturing environments such as production lines are dynamic and evolve over time due to new requirements (new customer needs, conformance to standards, maintenance, etc.) or due to the anomalies detected. When an evolution happens (e.g., new devices are introduced), monitoring systems must be aware of it in order to inform the user and to provide updated and reliable information. In this article, CALENDAR is presented, a software module for a monitoring system that addresses ICPS evolutions. The solution is based on a data metamodel that captures the structure of an ICPS in different timestamps. By comparing the data model in two subsequent timestamps, CALENDAR is able to detect and effectively classify the evolution of ICPSs at runtime to finally generate alerts about the detected evolution. In order to evaluate CALENDAR with different ICPS topologies (e.g., different ICPS sizes), a scalability test was performed considering the information captured from the production lines domain.

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Context-aware reconfiguration in evolving software product lines

Cited by 19 publications

References 36 publications

Reviewing Diagnosis Solutions for Valid Product Configurations in the Automated Analysis of Feature Models

Reviewing Diagnosis Solutions for Valid Product Configurations in the Automated Analysis of Feature Models

Anomaly Detection and Explanation in Context-Aware Software Product Lines

Industrial Cyber-Physical System Evolution Detection and Alert Generation

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