Isolated Features Detection in Feature Models

Feature models are commonly used to describe software product lines in terms of features. Features are linked by relations, which may introduce errors in the model. This paper gives a description of isolated features and states the detection of them, as the first step in their treatment. Two implementations are given to automatically support isolated features detection and a third one that uses both and improves the performance

CONFIGEN: A tool for managing configuration options

This paper introduces CONFIGEN, a tool that helps modularizing software. CONFIGEN allows the developer to select a set of elementary components for his software through an interactive interface. Configuration files for use by C/assembly code and Makefiles are then automatically generated, and we successfully used it as a helper tool for complex system software refactoring. CONFIGEN is based on propositional logic, and its implementation faces hard theoretical problems.Comment: In Proceedings LoCoCo 2010, arXiv:1007.083

On the structure of problem variability: From feature diagrams to problem frames

Requirements for product families are expressed in terms of commonality and variability. This distinction allows early identification of an appropriate software architecture and opportunities for software reuse. Feature diagrams provide intuitive notations and techniques for representing requirements in product line development. In this paper, we observe that feature diagrams tend to obfuscate three important descriptions: requirements, domain properties and specifications. As a result, feature diagrams do not adequately capture the problem structures that underlie variability, and inform the solution structures of their complexity. With its emphasis on separation of the three descriptions, the problem frames approach provides a conceptual framework for a more detailed analysis of variability and its structure. With illustrations from an example, we demonstrate how problem frames analysis of variability can augment feature diagrams

Subconjuntos Mínimos de Corrección para explicar características muertas en Modelos de Líneas de Productos. El caso de los Modelos de Características

Aprovechar los beneficios que ofrecen las líneas de productos depende, entre otros aspectos, de la calidad de los modelos que representan cada línea de productos. Una parte de la calidad consiste en asegurar que los Modelos de Líneas de Productos (MLPs) se encuentran libres de defectos. Un tipo de defecto de los MLPs son las características muertas, ellas son elementos reutilizables que no están presente en ningún producto configurado a partir del MLPs. Cuando las características muertas aparecen, quien crea los MLPs necesita herramientas que le permitan identificar por qué se presentan las características muertas y cómo podría corregirse el modelo. Sin embargo, aunque muchos trabajos en la literatura identifican características muertas, pocos explican por qué se originan o lo explican de manera incompleta. En este artículo se propone un nuevo método para explicar por qué se presentan características muertas en un MLP expresado con la notación modelos de características. Nuestra explicación consiste en identificar diferentes subconjuntos de elementos que podrían ser modificados para corregir el modelo cada que se presente una característica muerta. Esta explicación ofrece al modelador información completa sobre cómo corregir el modelo para cada característica muerta encontrada

Using Constraint Programming to Verify DOPLER Variability Models

Software product lines are typically developed using model-based approaches. Models are used to guide and automate key activities such as the derivation of products. The verification of product line models is thus essential to ensure the consistency of the derived products. While many authors have proposed approaches for verifying feature models there is so far no such approach for decision models. We discuss challenges of analyzing and verifying decision-oriented DOPLER variability models. The manual verification of these models is an error-prone, tedious, and sometimes infeasible task. We present a preliminary approach that converts DOPLER variability models into constraint programs to support their verification. We assess the feasibility of our approach by identifying defects in two existing variability models

Conformance Checking with Constraint Logic Programming: The Case of Feature Models

Developing high quality systems depends on developing high quality models. An important facet of model quality is their consistency with respect to their meta-model. We call the verification of this quality the conformance checking process. We are interested in the conformance checking of Product Line Models (PLMs). The problem in the context of product lines is that product models are not created by instantiating a meta-model: they are derived from PLMs. Therefore it is usually at the level of PLMs that conformance checking is applied. On the semantic level, a PLM is defined as the collection of all the product models that can be derived from it. Therefore checking the conformance of the PLM is equivalent to checking the conformance of all the product models. However, we would like to avoid this naïve approach because it is not scalable due to the high number of models. In fact, it is even sometimes infeasible to calculate the number of product models of a PLM. Despite the importance of PLM conformance checking, very few research works have been published and tools do not adequately support it. In this paper, we present an approach that employs Constraint Logic Programming as a technology on which to build a PLM conformance checking solution. The paper demonstrates the approach with feature models, the de facto standard for modeling software product lines. Based on an extensive literature review and an empirical study, we identified a set of 9 conformance checking rules and implemented them on the GNU Prolog constraints solver. We evaluated our approach by applying our rules to 50 feature models of sizes up to 10000 features. The evaluation showed that our approach is effective and scalable to industry size models

Hacia una meta-herramienta de análisis automático de modelos de variabilidad

El análisis automático de modelos de variabilidad es una actividad clave dentro de la gestión de la variabilidad. Existe un variado número de investigaciones y de enfoques que se han centrado en esta temática, fundamentalmente debido a que el hecho de determinar la validez de los modelos e identificar los problemas que contiene tanto a nivel de la definición de una línea de productos de software así como la instanciación y generación de productos en etapas tempranas, agiliza el desarrollo de la misma. Estos numerosos enfoques presentan diferentes tipos de modelos con distintas reglas, elementos y dependencias; diferentes procesos de validación y diversas reglas lógicas que soportan el análisis, así como diferentes solver (resolvedores lógicos) que determinan la validez de los modelos. Teniendo en cuenta este contexto, en este trabajo presentamos una Meta-herramienta de análisis automático basada en un proceso general, con el objetivo de proveer la suficiente flexibilidad que permita ser adaptada a diferentes modelos, reglas y resolvedores lógicos.XIV Workshop de Ingeniería de Software (WIS).Red de Universidades con Carreras en Informática (RedUNCI

CASE Tool support for variability management in software product lines

Software product lines (SPL) aim at reducing time-to-market and increasing software quality through extensive, planned reuse of artifacts. An essential activity in SPL is variability management, i.e., defining and managing commonality and variability among member products. Due to the large scale and complexity of today’s software-intensive systems, variability management has become increasingly complex to conduct. Accordingly, tool support for variability management has been gathering increasing momentum over the last few years and can be considered a key success factor for developing and maintaining SPLs. While several studies have already been conducted on variability management, none of these analyzed the available tool support in detail. In this work, we report on a survey in which we analyzed 37 existing variability management tools identified using a systematic literature review to understand the tools’ characteristics, maturity, and the challenges in the field. We conclude that while most studies on variability management tools provide a good motivation and description of the research context and challenges, they often lack empirical data to support their claims and findings. It was also found that quality attributes important for the practical use of tools such as usability, integration, scalability, and performance were out of scope for most studies

Defects in Product Line Models and How to Identify Them

This chapter is about generic (language-independent) verification criteria of product line models, its identification, formalisation, categorization, implementation with constraint programming techniques and its evaluation on several industrial and academic product line models represented with several languages