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MDD and MDA approaches require capturing the behavior of UML models in sufficient detail so that the models can be automatically implemented/executed in the production environment. With this purpose, Action Semantics (AS) were added to the UML specification as the fundamental unit of behavior specification. Actions are the basis for defining the fine-grained behavior of operations, activity diagrams, interaction diagrams and state machines. Unfortunately, current proposals devoted to the verification of behavioral schemas tend to skip the analysis of the actions they may include. The main goal of this paper is to cover this gap by presenting several techniques aimed at verifying AS specifications. Our techniques are based on the static analysis of the dependencies between the different actions included in the behavioral schema. For incorrect specifications, our method returns a meaningful feedback that helps repairing the inconsistency.

Feature Models (FMs) are a mechanism to model variability among a family of closely related software products, i.e. a software product line (SPL). Analysis of FMs using formal methods can reveal defects in the specification such as inconsistencies that cause the product line to have no valid products.A popular framework used in research for FM analysis is Alloy, a light-weight formal modeling notation equipped with an efficient model finder. Several works in the literature have proposed different strategies to encode and analyze FMs using Alloy. However, there is little discussion on the relative merits of each proposal, making it difficult to select the most suitable encoding for a specific analysis need. In this paper, we describe and compare those strategies according to various criteria such as the expressivity of the FM notation or the efficiency of the analysis. This survey is the first comparative study of research targeted towards using Alloy for FM analysis.This review aims to identify all the best practices on the use of Alloy, as a part of a framework for the automated extraction and analysis of rich FMs from natural language requirement specifications.

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