Clafer: Lightweight Modeling of Structure, Behaviour, and Variability

Embedded software is growing fast in size and complexity, leading to intimate mixture of complex architectures and complex control. Consequently, software specification requires modeling both structures and behaviour of systems. Unfortunately, existing languages do not integrate these aspects well, usually prioritizing one of them. It is common to develop a separate language for each of these facets. In this paper, we contribute Clafer: a small language that attempts to tackle this challenge. It combines rich structural modeling with state of the art behavioural formalisms. We are not aware of any other modeling language that seamlessly combines these facets common to system and software modeling. We show how Clafer, in a single unified syntax and semantics, allows capturing feature models (variability), component models, discrete control models (automata) and variability encompassing all these aspects. The language is built on top of first order logic with quantifiers over basic entities (for modeling structures) combined with linear temporal logic (for modeling behaviour). On top of this semantic foundation we build a simple but expressive syntax, enriched with carefully selected syntactic expansions that cover hierarchical modeling, associations, automata, scenarios, and Dwyer's property patterns. We evaluate Clafer using a power window case study, and comparing it against other notations that substantially overlap with its scope (SysML, AADL, Temporal OCL and Live Sequence Charts), discussing benefits and perils of using a single notation for the purpose

An automated Model-based Testing Approach in Software Product Lines Using a Variability Language.

This paper presents the application of an automated testing approach for Software Product Lines (SPL) driven by its state-machine and variability models. Context: Model-based testing provides a technique for automatic generation of test cases using models. Introduction of a variability model in this technique can achieve testing automation in SPL. Method: We use UML and CVL (Common Variability Language) models as input, and JUnit test cases are derived from these models. This approach has been implemented using the UML2 Eclipse Modeling platform and the CVL-Tool. Validation: A model checking tool prototype has been developed and a case study has been performed. Conclusions: Preliminary experiments have proved that our approach can find structural errors in the SPL under test. In our future work we will introduce Object Constraint Language (OCL) constraints attached to the input UML mode

A modular metamodel and refactoring rules to achieve software product line interoperability.

Emergent application domains, such as cyber–physical systems, edge computing or industry 4.0. present a high variability in software and hardware infrastructures. However, no single variability modeling language supports all language extensions required by these application domains (i.e., attributes, group cardinalities, clonables, complex constraints). This limitation is an open challenge that should be tackled by the software engineering field, and specifically by the software product line (SPL) community. A possible solution could be to define a completely new language, but this has a high cost in terms of adoption time and development of new tools. A more viable alternative is the definition of refactoring and specialization rules that allow interoperability between existing variability languages. However, with this approach, these rules cannot be reused across languages because each language uses a different set of modeling concepts and a different concrete syntax. Our approach relies on a modular and extensible metamodel that defines a common abstract syntax for existing variability modeling extensions. We map existing feature modeling languages in the SPL community to our common abstract syntax. Using our abstract syntax, we define refactoring rules at the language construct level that help to achieve interoperability between variability modeling languages.Work supported by the projects MEDEA RTI2018-099213-B-I00, IRIS PID2021-122812OB-I00 (co-financed by FEDER funds), Rhea P18-FR-1081 (MCI/AEI/FEDER, UE), LEIA UMA18-FEDERIA-157, and DAEMON H2020-101017109. // Funding for open access: Universidad de Málaga / CBUA

Investigating styles in variability modeling: Hierarchical vs. constrained styles

Context: A common way to represent product lines is with variability modeling. Yet, there are different ways to extract and organize relevant characteristics of variability. Comprehensibility of these models and the ease of creating models are important for the efficiency of any variability management approach. Objective: The goal of this paper is to investigate the comprehensibility of two common styles to organize variability into models - hierarchical and constrained - where the dependencies between choices are specified either through the hierarchy of the model or as cross-cutting constraints, respectively. Method: We conducted a controlled experiment with a sample of 90 participants who were students with prior training in modeling. Each participant was provided with two variability models specified in Common Variability Language (CVL) and was asked to answer questions requiring interpretation of provided models. The models included 9 to 20 nodes and 8 to 19 edges and used the main variability elements. After answering the questions, the participants were asked to create a model based on a textual description. Results: The results indicate that the hierarchical modeling style was easier to comprehend from a subjective point of view, but there was also a significant interaction effect with the degree of dependency in the models, that influenced objective comprehension. With respect to model creation, we found that the use of a constrained modeling style resulted in higher correctness of variability models. Conclusions: Prior exposure to modeling style and the degree of dependency among elements in the model determine what modeling style a participant chose when creating the model from natural language descriptions. Participants tended to choose a hierarchical style for modeling situations with high dependency and a constrained style for situations with low dependency. Furthermore, the degree of dependency also influences the comprehension of the variability model