Domain Specific Languages for Managing Feature Models: Advances and Challenges

International audienceManaging multiple and complex feature models is a tedious and error-prone activity in software product line engineering. Despite many advances in formal methods and analysis techniques, the supporting tools and APIs are not easily usable together, nor unified. In this paper, we report on the development and evolution of the Familiar Domain-Specific Language (DSL). Its toolset is dedicated to the large scale management of feature models through a good support for separating concerns, composing feature models and scripting manipulations. We overview various applications of Familiar and discuss both advantages and identified drawbacks. We then devise salient challenges to improve such DSL support in the near future

Lazy product discovery in huge configuration spaces

Highly-configurable software systems can have thousands of inter-dependent configuration options across different subsystems. In theresulting configuration space, discovering a valid product configu-ration for some selected options can be complex and error prone.The configuration space can be organized using a feature model,fragmented into smaller interdependent feature models reflectingthe configuration options of each subsystem.We propose a method for lazy product discovery in large frag-mented feature models with interdependent features. We formalizethe method and prove its soundness and completeness. The evalu-ation explores an industrial-size configuration space. The resultsshow that lazy product discovery has significant performance ben-efits compared to standard product discovery, which in contrastto our method requires all fragments to be composed to analyzethe feature model. Furthermore, the method succeeds when moreefficient, heuristics-based engines fail to find a valid configuration

Synthesis of Attributed Feature Models From Product Descriptions

International audienceMany real-world product lines are only represented as non-hierarchical collections of distinct products, described by their configuration values. As the manual preparation of feature models is a tedious and labour-intensive activity, some techniques have been proposed to automatically generate boolean feature models from product descriptions. However , none of these techniques is capable of synthesizing feature attributes and relations among attributes, despite the huge relevance of attributes for documenting software product lines. In this paper, we introduce for the first time an algorithmic and parametrizable approach for computing a legal and appropriate hierarchy of features, including feature groups, typed feature attributes, domain values and relations among these attributes. We have performed an empirical evaluation by using both randomized configuration matrices and real-world examples. The initial results of our evaluation show that our approach can scale up to matrices containing 2,000 attributed features, and 200,000 distinct configurations in a couple of minutes

Mining domain knowledge from app descriptions

Domain analysis aims at obtaining knowledge to a particular domain in the early stage of software development. A key challenge in domain analysis is to extract features automatically from related product artifacts. Compared with other kinds of artifacts, high volume of descriptions can be collected from app marketplaces (such as Google Play and Apple Store) easily when developing a new mobile application (App), so it is essential for the success of domain analysis to obtain features and relationship from them using data technologies. In this paper, we propose an approach to mine domain knowledge from App descriptions automatically. In our approach, the information of features in a single app description is firstly extracted and formally described by a Concern-based Description Model (CDM), this process is based on predefined rules of feature extraction and a modified topic modeling method; then the overall knowledge in the domain is identified by classifying, clustering and merging the knowledge in the set of CDMs and topics, and the results are formalized by a Data-based Raw Domain Model (DRDM). Furthermore, we propose a quantified evaluation method for prioritizing the knowledge in DRDM. The proposed approach is validated by a series of experiments

Lazy Product Discovery in Huge Configuration Spaces

Highly-configurable software systems can have thousands of interdependent configuration options across different subsystems. In the resulting configuration space, discovering a valid product configuration for some selected options can be complex and error prone. The configuration space can be organized using a feature model, fragmented into smaller interdependent feature models reflecting the configuration options of each subsystem. We propose a method for lazy product discovery in large fragmented feature models with interdependent features. We formalize the method and prove its soundness and completeness. The evaluation explores an industrial-size configuration space. The results show that lazy product discovery has significant performance benefits compared to standard product discovery, which in contrast to our method requires all fragments to be composed to analyze the feature model. Furthermore, the method succeeds when more efficient, heuristics-based engines fail to find a valid configuration

Extraction and evolution of architectural variability models in plugin-based systems

International audienceVariability management is a key issue when building and evolving software-intensive systems, making it possible to extend, confi gure, customize and adapt such systems to customers' needs and speci fic deployment contexts. A wide form of variability can be found in extensible software systems, typically built on top of plugin-based architectures that o ffer a (large) number of con figuration options through plugins. In an ideal world, a software architect should be able to generate a system variant on-demand, corresponding to a particular assembly of plugins. To this end, the variation points and constraints between architectural elements should be properly modeled and maintained over time (i.e., for each version of an architecture). A crucial, yet error-prone and time-consuming, task for a software architect is to build an accurate representation of the variability of an architecture, in order to prevent unsafe architectural variants and reach the highest possible level of flexibility. In this article, we propose a reverse engineering process for producing a variability model (i.e., a feature model) of a plugin-based architecture. We develop automated techniques to extract and combine di fferent variability descriptions, including a hierarchical software architecture model, a plugin dependency model and the software architect knowledge. By computing and reasoning about diff erences between versions of architectural feature models, software architect can control both the variability extraction and evolution processes. The proposed approach has been applied to a representative, large-scale plugin-based system (FraSCAti), considering diff erent versions of its architecture. We report on our experience in this context

Explicitly Integrated Architecture - An Approach for Integrating Software Architecture Model Information with Program Code

Software-Architekturspezifikationen und -Implementierungen sind zwei Sichtweisen auf Softwarearchitektur. Sie beschreiben gemeinsame Aspekte, wie z.B. die Existenz und Verbindung von Komponenten. Die Spezifikation fügt Informationen zum Design, zur Kommunikation und zur Analyse hinzu. Die Implementierung beschreibt stattdessen zusätzlich Details für ein ausführbares System. Die Konsistenz zwischen diesen Darstellungen manuell zu verwalten, ist schwierig und fehleranfällig. Diese Arbeit stellt einen Ansatz vor, der Informationen der Architekturspezifikation vollständig in die Implementierung integriert, sodass die Spezifikation als eigenständiges Artefakt nicht mehr notwendig ist. Das Tool Codeling extrahiert die integrierte Architekturspezifikation in unterschiedlichen Sprachen aus dem Code und propagiert Änderungen in dieser Spezifikation automatisch an den Code zurück.Specifications and implementations are both viewpoints upon software architecture. Besides common aspects, the specification adds information for design, communication, or analysis, while the implementation adds details for an executable system instead. Managing the consistency between these representations manually is difficult and error-prone. This thesis presents an approach, that completely integrates architecture specifications with the implementation, so that separate specification artifacts are not necessary anymore. The tool Codeling extracts integrated architecture specifications in multiple languages from code, and automatically propagates changes in these specifications back to the code