Abstract. Feature models are a widespread means to represent commonality and variability in software product lines. As is the case for other kinds of models, computing and managing feature model differences is useful in various real-world situations. In this paper, we propose a set of novel differencing techniques that combine syntactic and semantic mechanisms, and automatically produce meaningful differences. Practitioners can exploit our results in various ways: to understand, manipulate, visualize and reason about differences. They can also combine them with existing feature model composition and decomposition operators. The proposed automations rely on satisfiability algorithms. They come with a dedicated language and a comprehensive environment. We illustrate and evaluate the practical usage of our techniques through a case study dealing with a configurable component framework.

International audienceMigrating legacy systems or deploying a new application to a cloud environment has recently become very trendy, because the number of cloud providers available is still increasing. These cloud environments provide a wide range of resources at different levels of functionality, which must be appropriately configured by stakeholders for the application to run properly. Handling this variability during the configuration and deployment stages is known as a complex and error-prone process, usually made in an ad hoc manner. In this paper, we propose SALOON, a software product lines-based platform to face these issues. We describe the architecture of the SALOON platform, which relies on feature models combined with a domain model used to select among cloud environments a well-suited one. SALOON supports stakeholders while configuring the selected cloud environment in a consistent way and automates the deployment of such configurations through the generation of executable configuration scripts. This paper also reports on some experiments, showing that using SALOON significantly reduces time to configure a cloud environment compared with a manual approach and provides a reliable way to find a correct and suitable configuration. Moreover, our empirical evaluation shows that our approach is effective and scalable to properly deal with a significant number of cloud environments