Roberto E. Lopez-Herrejon

Enhancing Clone-and-Own with Systematic Reuse for Developing Software Variants

To keep pace with the increasing demand for custom-tailored software systems, companies often apply a practice called clone-and-own, whereby a new variant of a software system is built by coping and adapting existing variants. Instead of a single and configurable system, clone-and-own leads to ad hoc product portfolios of multiple yet similar variants that soon become impossible to maintain effectively. Clone-and-own has widespread industrial use because it requires no major upfront investments and is intuitive, but it lacks a methodology for systematic reuse. In this work we propose ECCO (Extraction and Composition for Clone-and-Own), a novel approach to enhance clone and-own that actively supports the development and maintenance of software product variants. A software engineer selects the desired features and ECCO finds the proper software artifacts to reuse and then provides guidance during the manual completion by hinting which software artifacts may need adaptation. We evaluated our approach on 6 case studies, covering 402 variants having up to 344KLOC, and found that precision and recall of composed products quickly reach a near optimum (>95% reuse).

A systematic mapping study of search-based software engineering for software product lines

ContextSearch-Based Software Engineering (SBSE) is an emerging discipline that focuses on the application of search-based optimization techniques to software engineering problems. Software Product Lines (SPLs) are families of related software systems whose members are distinguished by the set of features each one provides. SPL development practices have proven benefits such as improved software reuse, better customization, and faster time to market. A typical SPL usually involves a large number of systems and features, a fact that makes them attractive for the application of SBSE techniques which are able to tackle problems that involve large search spaces. ObjectiveThe main objective of our work is to identify the quantity and the type of research on the application of SBSE techniques to SPL problems. More concretely, the SBSE techniques that have been used and at what stage of the SPL life cycle, the type of case studies employed and their empirical analysis, and the fora where the research has been published. MethodA systematic mapping study was conducted with five research questions and assessed 77 publications from 2001, when the term SBSE was coined, until 2014. ResultsThe most common application of SBSE techniques found was testing followed by product configuration, with genetic algorithms and multi-objective evolutionary algorithms being the two most commonly used techniques. Our study identified the need to improve the robustness of the empirical evaluation of existing research, a lack of extensive and robust tool support, and multiple avenues worthy of further investigation. ConclusionsOur study attested the great synergy existing between both fields, corroborated the increasing and ongoing interest in research on the subject, and revealed challenging open research questions.

Reverse engineering feature models with evolutionary algorithms: an exploratory study

Successful software evolves, more and more commonly, from a single system to a set of system variants tailored to meet the similiar and yet different functionality required by the distinct clients and users. Software Product Line Engineering (SPLE) is a software development paradigm that has proven effective for coping with this scenario. At the core of SPLE is variability modeling which employs Feature Models (FMs) as the de facto standard to represent the combinations of features that distinguish the systems variants. Reverse engineering FMs consist in constructing a feature model from a set of products descriptions. This research area is becoming increasingly active within the SPLE community, where the problem has been addressed with different perspectives and approaches ranging from analysis of configuration scripts, use of propositional logic or natural language techniques, to ad hoc algorithms. In this paper, we explore the feasibility of using Evolutionary Algorithms (EAs) to synthesize FMs from the feature sets that describe the system variants. We analyzed 59 representative case studies of different characteristics and complexity. Our exploratory study found that FMs that denote proper supersets of the desired feature sets can be obtained with a small number of generations. However, reducing the differences between these two sets with an effective and scalable fitness function remains an open question. We believe that this work is a first step towards leveraging the extensive wealth of Search-Based Software Engineering techniques to address this and other variability management challenges.

On extracting feature models from sets of valid feature combinations

Rather than developing individual systems, Software Product Line Engineering develops families of systems. The members of the software family are distinguished by the features they implement and Feature Models (FMs) are the de facto standard for defining which feature combinations are considered valid members. This paper presents an algorithm to automatically extract a feature model from a set of valid feature combinations, an essential development step when companies, for instance, decide to convert their existing product variations portfolio into a Software Product Line. We performed an evaluation on 168 publicly available feature models, with 9 to 38 features and up to 147456 feature combinations. From the generated feature combinations of each of these examples, we reverse engineered an equivalent feature model with a median performance in the low milliseconds.

Reverse Engineering Feature Models from Programs' Feature Sets

Successful software is more and more rarely developed as a one-of-a-kind system. Instead, different system variants are built from a common set of assets and customized for catering to the different functionality or technology needs of the distinct clients and users. The Software Product Line Engineering (SPLE) paradigm has proven effective to cope with the variability described for this scenario. However, evolving a Software Product Line (SPL) from a family of systems is not a simple endeavor. A crucial requirement is accurately capturing the variability present in the family of systems and representing it with Feature Models (FMs), the de facto standard for variability modeling. Current research has focused on extracting FMs from configuration scripts, propositional logic expressions or natural language. In contrast, in this short paper we present an algorithm that reverse engineers a basic feature model from the feature sets which describe the features each system provides. We perform an evaluation of our approach using several case studies and outline the issues that still need to be addressed.

Applying multiobjective evolutionary algorithms to dynamic software product lines for reconfiguring mobile applications

Mobile applications require to self-adapt their behavior to context changes.We propose a DSPL approach to manage variability at runtime.Configurations are generated using multiobjective evolutionary algorithms.We apply a fix operator to generate only valid configurations at runtime.We demonstrate that this approach is suitable for mobile environments. Mobile applications require dynamic reconfiguration services (DRS) to self-adapt their behavior to the context changes (e.g., scarcity of resources). Dynamic Software Product Lines (DSPL) are a well-accepted approach to manage runtime variability, by means of late binding the variation points at runtime. During the systems execution, the DRS deploys different configurations to satisfy the changing requirements according to a multiobjective criterion (e.g., insufficient battery level, requested quality of service). Search-based software engineering and, in particular, multiobjective evolutionary algorithms (MOEAs), can generate valid configurations of a DSPL at runtime. Several approaches use MOEAs to generate optimum configurations of a Software Product Line, but none of them consider DSPLs for mobile devices. In this paper, we explore the use of MOEAs to generate at runtime optimum configurations of the DSPL according to different criteria. The optimization problem is formalized in terms of a Feature Model (FM), a variability model. We evaluate six existing MOEAs by applying them to 12 different FMs, optimizing three different objectives (usability, battery consumption and memory footprint). The results are discussed according to the particular requirements of a DRS for mobile applications, showing that PAES and NSGA-II are the most suitable algorithms for mobile environments.

Multi-objective Optimal Test Suite Computation for Software Product Line Pairwise Testing

Software Product Lines (SPLs) are families of related software products, which usually provide a large number of feature combinations, a fact that poses a unique set of challenges for software testing. Recently, many SPL testing approaches have been proposed, among them pair wise combinatorial techniques that aim at selecting products to test based on the pairs of feature combinations such products provide. These approaches regard SPL testing as an optimization problem where either coverage (maximize) or test suite size (minimize) are considered as the main optimization objective. Instead, we take a multi-objective view where the two objectives are equally important. In this exploratory paper we propose a zero-one mathematical linear program for solving the multi-objective problem and present an algorithm to compute the true Pareto front, hence an optimal solution, from the feature model of a SPL. The evaluation with 118 feature models revealed an interesting trade-off between reducing the number of constraints in the linear program and the runtime which opens up several venues for future research.

A first systematic mapping study on combinatorial interaction testing for software product lines

Software Product Lines (SPLs) are families of related software systems distinguished by the set of features each one provides. Over the past decades SPLs have been the subject of extensive research and application both in academia and industry. SPLs practices have proven benefits such as better product customization and reduced time to market. Testing SPLs pose additional challenges stemming from the typically large number of product variants which make it infeasible to test every single one of them. In recent years, there has been an extensive research on applying Combinatorial Interaction Testing (CIT) for SPL testing. In this paper we present the first systematic mapping study on this subject. Our research questions aim to gather information regarding the techniques that have been applied, the nature of the case studies used for their evaluation, and what phases of CIT have been addressed. Our goal is to identify common trends, gaps, and opportunities for further research and application.

A parallel evolutionary algorithm for prioritized pairwise testing of software product lines

Software Product Lines (SPLs) are families of related software systems, which provide different feature combinations. Different SPL testing approaches have been proposed. However, despite the extensive and successful use of evolutionary computation techniques for software testing, their application to SPL testing remains largely unexplored. In this paper we present the Parallel Prioritized product line Genetic Solver (PPGS), a parallel genetic algorithm for the generation of prioritized pairwise testing suites for SPLs. We perform an extensive and comprehensive analysis of PPGS with 235 feature models from a wide range of number of features and products, using 3 different priority assignment schemes and 5 product prioritization selection strategies. We also compare PPGS with the greedy algorithm prioritized-ICPL. Our study reveals that overall PPGS obtains smaller covering arrays with an acceptable performance difference with prioritized-ICPL.

Detecting inconsistencies in multi-view models with variability

Multi-View Modeling (MVM) is a common modeling practice that advocates the use of multiple, different and yet related models to represent the needs of diverse stakeholders. Of crucial importance in MVM is consistency checking — the description and verification of semantic relationships amongst the views. Variability is the capacity of software artifacts to vary, and its effective management is a core tenet of the research in Software Product Lines (SPL). MVM has proven useful for developing one-of-a-kind systems; however, to reap the potential benefits of MVM in SPL it is vital to provide consistency checking mechanisms that cope with variability. In this paper we describe how to address this need by applying Safe Composition — the guarantee that all programs of a product line are type safe. We evaluate our approach with a case study.