Feature Model Differences

International audienceFeature 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

Carrying Ideas from Knowledge-Based Configuration to Software Product Lines

Software variability modelling (SVM) has become a central concern in software product lines -- especially configurable software product lines (CSPL) require rigorous SVM. Dynamic SPLs, service oriented SPLs, and autonomous or pervasive systems are examples where CSPLs are applied. Knowledge-based configuration (KBC) is an established way to address variability modelling aiming for the automatic product configuration of physical products. Our aim was to study what major ideas from KBC can be applied to SVM, particularly in the context of CSPLs. Our main contribution is the identification of major ideas from KBC that could be applied to SVM. First, we call for the separation of types and instances. Second, conceptual clarity of modelling concepts, e.g., having both taxonomical and compositional relations would be useful. Third, we argue for the importance of a conceptual basis that provides a foundation for multiple representations, e.g., graphical and textual. Applying the insights and experiences embedded in these ideas may help in the development of modelling support for software product lines, particularly in terms of conceptual clarity and as a basis for tool support with a high level of automation.Peer reviewe

Alternating-time logic with imperfect recall

AbstractWe study here a variant of the alternating-time temporal logic (ATL) where each agent has a given memory. We show that it is an interesting compromise, rather realistic but with a reasonable complexity. In contrast, most models with perfect recall and imperfect information have an undecidable model-checking problem

Proving a conjecture of Andreka on temporal logic

In [3], a large number of completeness results about variants of discrete linear-time temporal logic are obtained. One of them is left as an open problem: the completeness of the logic of initially and next, for which a deductive system is proposed. This simple logic is of practical importance, since the proof of program invariants only require these modalities. We show here that the conjectured medium completeness of this system indeed holds; further, we show that this decision problem is PSPACE-complete, while deciding the problem of validity is only NP-complete. 1 Introduction The temporal logic has been created by Prior [13, 14]. Its importance for program construction and verification was introduced by [12], and has since then been a topic of intensive research [9, 1, 4, 6, 7]. The creation of complete inference systems, as required for proofs of programs, is an important sub-topic. A rich propositional discrete-time linear temporal logic is treated in [9]: we shall use the same ..