Carlos Cetina

Autonomic Computing through Reuse of Variability Models at Runtime: The Case of Smart Homes

Our research shows that autonomic behavior can be achieved by leveraging variability models at runtime. In this way, the modeling effort made at design time is not only useful for producing the system but also provides a richer semantic base for autonomic behavior during execution. The use of variability models at runtime brings new opportunities for autonomic capabilities by reutilizing the efforts invested at design time. Our proposed approach has two aspects: reuse of design knowledge to achieve AC and reuse of existing model-management technologies at runtime. We developed the Model-Based Reconfiguration Engine (MoRE) to implement model-management operations. Our research demonstrates the approachs feasibility for smart homes, especially for self-healing and -configuring capabilities.

Applying Software Product Lines to Build Autonomic Pervasive Systems

Increasingly, software needs to dynamically adapt its behavior at run-time in response to changing conditions in the supporting computing infrastructure and in the surrounding physical environment. This paper introduces an approach for the design of pervasive SPLs that is based on model driven development (MDD) and Variability Modeling principles. Variability models are interpreted at run-time to reconfigure pervasive systems according to fluctuations in the environment. This approach helps to improve Pervasive SPLs to produce software that adapts itself in an autonomic way. We have developed an adaptive pervasive system for smart homes to validate this approach.

Developing Mobile Business Processes for the Internet of Things

En la presente comunicación se describen publicación del artículo “Developing Mobile Workflow support in The Internet of Things” en la revista IEEE Pervasive Computing en 2010.

Using Feature Models for Developing Self-Configuring Smart Homes

Increasingly, Smart Homes should dynamically reconfigure their services at run-time in response to changing conditions in the user actions, and in the surrounding physical environment. Considering the high heterogeneity of technologies and user requirements involved in Smart Homes, these systems become difficult to adjust to the specific user needs. This paper introduces an approach to build selfconfiguring Smart Homes using Features models. We apply Feature modelling to specify the different ways in which the system can evolve from an abstract perspective. We show an infrastructure for transforming these Feature Models to an executable Reconfiguration Plan. In this way, the entire development process is taken into account. Since the models forming the basis for reconfiguration are available at design time, we are able to check the validity of configurations according to context conditions.

Tool Support for Model Driven Development of Pervasive Systems

This work presents the PervML Generative Tool (PervGT) that supports a model driven method for the development of pervasive services in ubiquitous environments. The tool, which is based on the Eclipse platform, provides facilities for the graphical description of pervasive systems using PervML, a UML-like modeling language. Once the pervasive system is specified, the PervML model is used as input to a transformation engine that generates source code and other implementation assets. This generated code extends an OSGi-based framework in order to build the final pervasive applications

Automating the variability formalization of a model family by means of common variability language

The aim of domain engineering process is to define and realise the commonality and variability of a Software Product Line. In the context of a family of models, spotting the commonalities and differences may become cumbersome and error prone as the number of models and its complexity increases. This work presents an approach to automate the formalization of variability in a given family of models. As output, the variability is made explicit in terms of Common Variability Language. The model commonalities and differences are specified as placements over a base model and replacements in a model library. The resulting Software Product Line (SPL) enables the derivation of new product models by reusing the extracted model fragments. Furthermore, the SPL can be evolved by the creation of new models, which are in turn automatically decomposed as model fragments of the SPL. The approach has been validated with our industrial partner (BSH), an induction hobs company. Finally, we present five different evolution scenarios encountered during the validation.

Building software product lines from conceptualized model patterns

Software Product Lines (SPLs) can be established from a set of similar models. Establishing the Product Line by mechanically finding model differences may not be the best approach. The identified model fragments may not be seen as recognizable units by the application engineers. We propose to identify model patterns by human-in-the-loop and conceptualize them as reusable model fragments. The approach provides the means to identify and extract those model patterns and further apply them to existing product models. Model fragments obtained by applying our approach seem to perform better than mechanically found ones. It turns out that the repetition of a fragment does not guarantee its relevance as reusable asset for the SPL engineers and vice versa, a fragment that has not been repeated yet, may be relevant as a reusable asset. We have validated these ideas with our industrial partner BSH, an induction hobs manufacturer that generates the firmware of their products from a model-driven SPL.

Designing and prototyping dynamic software product lines: techniques and guidelines

Dynamic Software Product Lines (DSPL) encompass systems that are capable of modifying their own configuration with respect to changes in their operating environment by using run-time reconfigurations. A failure in these reconfigurations can directly impact the user experience since the reconfigurations are performed when the system is already under the users control. Prototyping DSPLs at an early development stage can help to pinpoint potential issues and optimize design. In this work, we identify and addresses two challenges associated with the involvement of human subjects in DSPL prototyping: enabling DSPL users to (1) trigger the run-time reconfigurations and to (2) understand the effects of the reconfigurations. These techniques have been applied with the participation of human subjects by means of a Smart Hotel case study which was deployed with real devices. The application of these techniques reveals DSPL-design issues with recovering from a failed reconfiguration or a reconfiguration triggered by mistake. To address these issues, we discuss some guidelines learned in the Smart Hotel case study.

Feature Location in Model-Based Software Product Lines Through a Genetic Algorithm

When following an extractive approach to build a model-based Software Product Line SPL from a set of existing products, features have to be located across the product models. The approaches that produce best results combine model comparisons with the knowledge from the domain experts to locate the features. However, when the domain expert fails to provide accurate information, the semi-automated approach faces challenges. To cope with this issue we propose a genetic algorithm to feature location in model-based SPLs. We have an oracle from an industrial environment that makes it possible to evaluate the results of the approaches. As a result, the proposed approach is able to provide solutions upon inaccurate information on part of the domain expert while the compared approach fails to provide a solution when the information provided by the domain expert is not accurate enough.

Prototyping Dynamic Software Product Lines to evaluate run-time reconfigurations

Dynamic Software Product Lines (DSPL) encompass systems that are capable of modifying their own behavior with respect to changes in their operating environment by using run-time reconfigurations. A failure in these reconfigurations can directly impact the user experience since the reconfigurations are performed when the system is already under the users control. In this work, we prototype a Smart Hotel DSPL to evaluate the reliability-based risk of the DSPL reconfigurations, specifically, the probability of malfunctioning (Availability) and the consequences of malfunctioning (Severity). This DSPL prototype was performed with the participation of human subjects by means of a Smart Hotel case study which was deployed with real devices. Moreover, we successfully identified and addressed two challenges associated with the involvement of human subjects in DSPL prototyping: enabling participants to (1) trigger the run-time reconfigurations and to (2) understand the effects of the reconfigurations. The evaluation of the case study reveals positive results regarding both Availability and Severity. However, the participant feedback highlights issues with recovering from a failed reconfiguration or a reconfiguration triggered by mistake. To address these issues, we discuss some guidelines learned in the case study. Finally, although the results achieved by the DSPL may be considered satisfactory for its particular domain, DSPL engineers must provide users with more control over the reconfigurations or the users will not be comfortable with DSPLs.