Jaime Font

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.

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.

Feature location in models through a genetic algorithm driven by information retrieval techniques

In this work we propose a feature location approach that targets models as the feature realization artifacts. The approach combines Genetic Algorithms and Information Retrieval techniques. Given a model and a feature description, model fragments extracted from the model are evolved using genetic operations. Then, Formal Concept Analysis is used to cluster the model fragments based on their common attributes into feature realization candidates. Finally, Latent Semantic Analysis is used to rank the candidates based on the similarity with the feature description. As a result, the genetic algorithm evolves the population of model fragments to find the set of most suitable feature realizations. We have evaluated the approach with an industrial case study, locating features with precision and recall values around 90% (baseline obtains less than 40%). Finally, we provide recommendations on how to provide the input to the approach to improve the location of features over the models.

Towards Memory-Aware Services and Browsing through Lifelogging Sensing

Every day we receive lots of information through our senses that is lost forever, because it lacked the strength or the repetition needed to generate a lasting memory. Combining the emerging Internet of Things and lifelogging sensors, we believe it is possible to build up a Digital Memory (Dig-Mem) in order to complement the fallible memory of people. This work shows how to realize the Dig-Mem in terms of interactions, affinities, activities, goals and protocols. We also complement this Dig-Mem with memory-aware services and a Dig-Mem browser. Furthermore, we propose a RFID Tag-Sharing technique to speed up the adoption of Dig-Mem. Experimentation reveals an improvement of the user understanding of Dig-Mem as time passes, compared to natural memories where the level of detail decreases over time.

Achieving Feature Location in Families of Models Through the Use of Search-Based Software Engineering

The application of search-based software engineering techniques to new problems is increasing. Feature location is one of the most important and common activities performed by developers during software maintenance and evolution. Features must be located across families of products and the software artifacts that realize each feature must be identified. However, when dealing with industrial software artifacts, the search space can be huge. We propose and compare five search algorithms to locate features over families of product models guided by latent semantic analysis (LSA), a technique that measures similarities between textual queries. The algorithms are applied to two case studies from our industrial partners (leading manufacturers of home appliances and rolling stock) and are compared in terms of precision and recall. Statistical analysis of the results is performed to provide evidence of the significance of the results. The combination of an evolutionary algorithm with LSA can be used to locate features in families of models from industrial scenarios such as the ones from our industrial partners.

Towards Feature Location in Models through a Learning to Rank Approach

In this work, we propose a feature location approach to discover software artifacts that implement the feature functionality in a model. Given a model and a feature description, model fragments extracted from the model and the feature description are encoded based on a domain ontology. Then, a Learning to Rank algorithm is used to train a classifier that is based on the model fragments and feature description encoded. Finally, the classifier assesses the similarity between a population of model fragments and the target feature being located to find the set of most suitable feature realizations. We have evaluated the approach with an industrial case study, locating features with mean precision and recall values of around 73.75% and 73.31%, respectively (the sanity check obtains less than 35%).

Feature Location Through the Combination of Run-Time Architecture Models and Information Retrieval

Eighty percent of the lifetime of a system is spent on maintenance activities. Feature location is one of the most important and common activities performed by developers during software maintenance. This work presents our approach for performing feature location by leveraging the use of architecture models at run-time. Specifically, the execution information is collected in the architecture model at run-time. Then, our approach performs an Information Retrieval technique at the model level. We have evaluated our approach in a Smart Hotel with its architecture model at run-time. We compared our architecture-model-based approach with a source-code-based approach. The rankings produced by the approaches indicate that since models are on a higher abstraction level than source code, they provide more accurate results. Our architecture-model-based approach ranks the relevant elements in the top ten positions of the ranking in 84 % of the cases; in the top positions in the ranking of the source-code-based approach, there are false positives associated with some programming patterns and true positives are spread between positions 12 and 100.

Achieving Knowledge Evolution in Dynamic Software Product Lines

Dynamic Software Product Lines (DSPLs) offer a strategy to deal with software changes that need to be handled at run-time. In response to context changes, a DSPL capitalize on knowledge about the architecture variability of the software system to shift between configurations. Similar to any other kind of software, a DSPL needs to evolve over time but current approaches require software engineers to manually perform the DSPL evolution. Our work addresses the evolution of the architecture variability that makes up the knowledge of the DSPL. Given a new version of the architecture variability, we calculate its configuration space and propose strategies that allow migration from the current version to the new version. Our strategy solves the collision of the realization layer resulting from the integration of the new version of the variability specification. We evaluate our dynamic evolution strategy using the Goal-Question-Metric method for a Smart Hotel case study with 239 possible configurations as starting point. Our experiment indicates that the proposed technique would enable automatic evolution in 9 out of 10 cases. In the rest of the cases, all of the DSPL configurations changed between the old and the new version, which frustrates an automatic evolution.

Addressing metamodel revisions in model-based software product lines

Metamodels evolve over time, which can break the conformance between the models and the metamodel. Model migration strategies aim to co-evolve models and metamodels together, but their application is not fully automatizable and is thus cumbersome and error prone. We introduce the Variable MetaModel (VMM) strategy to address the evolution of the reusable model assets of a model-based Software Product Line. The VMM strategy applies variability modeling ideas to express the evolution of the metamodel in terms of commonalities and variabilities. When the metamodel evolves, the models continue to conform to the VMM, avoiding the need for migration. We have applied both the traditional migration strategy and the VMM strategy to a retrospective case study that includes 13 years of evolution of our industrial partner, an induction hobs manufacturer. The comparison between the two strategies shows better results for the VMM strategy in terms of model indirection, automation, and trust leak.