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Software Product Lines (SPLs) are families of related software products developed from a common set of artifacts. Most existing analysis tools can be applied to a single product at a time, but not to an entire SPL. Some tools have been redesigned/re-implemented to support the kind of variability exhibited in SPLs, but this usually takes a lot of effort, and is error-prone. Declarative analyses written in languages like Datalog have been collectively lifted to SPLs in prior work [1], which makes the process of applying an existing declarative analysis to a product line more straightforward.In this paper, we take an existing declarative analysis (behaviour alteration) and apply it to a set of automotive software product lines from General Motors. We discuss the design of the analysis pipeline used in this process, present its scalability results, and provide a means to visualize the analysis results for a subset of products filtered by feature expression. We also reflect on some of the lessons learned throughout this project.

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We present an approach that improves the robustness of web service compositions enabling their recovery from failures that can happen at different execution times. We first present a taxonomy of failures as an overview of previous research works on the topic of fault recovery of service compositions. The resulting classification is used to propose our self-healing method for web service compositions. The proposed method, based on the refinement process of compositions, takes user preferences into account to generate the best possible recovering compositions. In order to validate our approach, we produced a prototype implementation capable of simulating and analysing different scenarios of faults. Our work introduces algorithms for generating synthetic compositions and web services. In this setting, the recovery time, the user preference degradation and the impact of different locations of failure are investigated under different strategies, namely local, partial or total recovery. These strategies represent different levels of intervention on the composition.

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