Model Management addresses the problem of managing an evolving collection of models, by capturing the relationships between models and providing well-defined operators to manipulate them. In this paper, we describe two such operators for manipulating hierarchical Statecharts: Match, for finding correspondences between models, and Merge, for combining models with respect to known correspondences between them. Our Match operator is heuristic, making use of both static and behavioural properties of the models to improve the accuracy of matching. Our Merge operator preserves the hierarchical structure of the input models, and handles differences in behaviour through parameterization. In this way, we automatically construct merges that preserve the semantics of Statecharts models. We illustrate and evaluate our work by applying our operators to AT&T telecommunication features.

Constructing comprehensive operational models of intended system behaviour is a complex and costly task. Consequently, practitioners have adopted techniques that support incremental elaboration of partial behaviour descriptions. A noteworthy example is the wide adoption of scenario-based notations such as message sequence charts. Scenario-based specifications are partial descriptions that can be incrementally elaborated to cover the system behaviour that is of interest. However, how should partial behavioural models described by different stakeholders with different viewpoints covering different aspects of behaviour be composed? How should partial models of component instances of the same type be put together?In this paper, we propose model merging as a general solution to these questions. We formally define model merging based on observational refinement and show that merging consistent models is a process that should result in a minimal common refinement. Because minimal common refinements are not guaranteed to be unique, we argue that the modeller should participate in the process of elaborating such a model. We also discuss the role of the least common refinement and the greatest lower bound of all minimal common refinements in this elaboration process. In addition, we provide algorithms for i) checking consistency between two models; ii) constructing their least common refinement if one exists; iii) supporting the construction of a minimal common refinement if there is no least common refinement.

This article introduces the concept of multi-valued model-checking and describes a multi-valued symbolic model-checker, χ Chek. Multi-valued model-checking is a generalization of classical modelchecking, useful for analyzing models that contain uncertainty (lack of essential information) or inconsistency (contradictory information, often occurring when information is gathered from multiple sources). Multi-valued logics support the explicit modeling of uncertainty and disagreement by providing additional truth values in the logic.This article provides a theoretical basis for multi-valued model-checking and discusses some of its applications. A companion article [Chechik et al. 2002b] describes implementation issues in detail. The model-checker works for any member of a large class of multi-valued logics. Our modeling language is based on a generalization of Kripke structures, where both atomic propositions and transitions between states may take any of the truth values of a given multi-valued logic. Properties are expressed in χ CTL, our multi-valued extension of the temporal logic CTL.We define the class of logics, present the theory of multi-valued sets and multi-valued relations used in our model-checking algorithm, and define the multi-valued extensions of CTL and Kripke structures. We explore the relationship between χ CTL and CTL, and provide a symbolic modelchecking algorithm for χ CTL. We also address the use of fairness in multi-valued model-checking. Finally, we discuss some applications of the multi-valued model-checking approach.

The rise in efficiency of Satisfiability Modulo Theories (SMT) solvers has created numerous uses for them in software verification, program synthesis, functional programming, refinement types, etc. In all of these applications, SMT solvers are used for generating satisfying assignments (e.g., a witness for a bug) or proving unsatisfiability/validity (e.g., proving that a subtyping relation holds). We are often interested in finding not just an arbitrary satisfying assignment, but one that optimizes (minimizes/maximizes) certain criteria. For example, we might be interested in detecting program executions that maximize energy usage (performance bugs), or synthesizing short programs that do not make expensive API calls. Unfortunately, none of the available SMT solvers offer such optimization capabilities.In this paper, we present SYMBA, an efficient SMT-based optimization algorithm for objective functions in the theory of linear real arithmetic (LRA). Given a formula ϕ and an objective function t, SYMBA finds a satisfying assignment of ϕ that maximizes the value of t. SYMBA utilizes efficient SMT solvers as black boxes. As a result, it is easy to implement and it directly benefits from future advances in SMT solvers. Moreover, SYMBA can optimize a set of objective functions, reusing information between them to speed up the analysis. We have implemented SYMBA and evaluated it on a large number of optimization benchmarks drawn from program analysis tasks. Our results indicate the power and efficiency of SYMBA in comparison with competing approaches, and highlight the importance of its multi-objective-function feature.

Feature location techniques aim at locating software artifacts that implement a specific program functionality, a.k.a. a feature. These techniques support developers during various activities such as software maintenance, aspect-or featureoriented refactoring, and others. For example, detecting artifacts that correspond to product line features can assist the transition from unstructured to systematic reuse approaches promoted by software product line engineering (SPLE). Managing features, as well as the traceability between these features and the artifacts that implement them, is an essential task of the SPLE domain engineering phase, during which the product line resources are specified, designed and implemented. In this chapter, we provide an overview of existing feature location techniques. We describe their implementation strategies and exemplify the techniques on a realistic use-case. We also discuss their properties, strengths and weaknesses and provide guidelines that can be used by practitioners when deciding which feature location technique to choose. Our survey shows that none of the existing feature location techniques are designed to consider families of related products and only treat different products of a product line as individual, unrelated entities. We thus discuss possible directions for leveraging SPLE architectures in order to improve the feature location process.