Variability Modeling Language Research Articles

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.

Modeling Variability in the Performance Perspective of Business Processes

The modeling and management of business processes often leads to the definition of several variants of the same process. This variability can be reflected in different process perspectives such as control-flow, data, resources or performance. The management of process variants can be a laborious, time-consuming and error-prone task since they require a high coordination in the management of each variant and in most cases this management is done manually. For this reason, many proposals have been developed to deal with the variability of business processes. However, none of them covers in detail the variability in the performance perspective, which is concerned with the definition of performance requirements usually specified as a set of Process Performance Indicators (PPIs). This variability can be reflected in the form of repetitive and redundant PPI definitions, and can lead to errors and inconsistencies in PPI definitions. To address this problem, in this article we propose a detailed PPI variability classification and a formalization of how PPIs can be modeled together with the variability of other process perspectives. To this end, we considered variability management approaches, called by restriction and by extension, and we illustrated our proposal by integrating it with existing variability modeling languages. An evaluation conducted in two scenarios shows the feasibility and usefulness of our proposal.

Modeling variability in the video domain: language and experience report

In an industrial project, we addressed the challenge of developing a software-based video generator such that consumers and providers of video processing algorithms can benchmark them on a wide range of video variants. This article aims to report on our positive experience in modeling, controlling, and implementing software variability in the video domain. We describe how we have designed and developed a variability modeling language, called VM, resulting from the close collaboration with industrial partners during 2 years. We expose the specific requirements and advanced variability constructs; we developed and used to characterize and derive variations of video sequences. The results of our experiments and industrial experience show that our solution is effective to model complex variability information and supports the synthesis of hundreds of realistic video variants. From the software language perspective, we learned that basic variability mechanisms are useful but not enough; attributes and multi-features are of prior importance; meta-information and specific constructs are relevant for scalable and purposeful reasoning over variability models. From the video domain and software perspective, we report on the practical benefits of a variability approach. With more automation and control, practitioners can now envision benchmarking video algorithms over large, diverse, controlled, yet realistic datasets (videos that mimic real recorded videos)—something impossible at the beginning of the project.

FLAME: a formal framework for the automated analysis of software product lines validated by automated specification testing

In a literature review on the last 20 years of automated analysis of feature models, the formalization of analysis operations was identified as the most relevant challenge in the field. This formalization could provide very valuable assets for tool developers such as a precise definition of the analysis operations and, what is more, a reference implementation, i.e., a trustworthy, not necessarily efficient implementation to compare different tools outputs. In this article, we present the FLAME framework as the result of facing this challenge. FLAME is a formal framework that can be used to formally specify not only feature models, but other variability modeling languages (VML s) as well. This reusability is achieved by its two-layered architecture. The abstract foundation layer is the bottom layer in which all VML-independent analysis operations and concepts are specified. On top of the foundation layer, a family of characteristic model layers—one for each VML to be formally specified—can be developed by redefining some abstract types and relations. The verification and validation of FLAME has followed a process in which formal verification has been performed traditionally by manual theorem proving, but validation has been performed by integrating our experience on metamorphic testing of variability analysis tools, something that has shown to be much more effective than manually designed test cases. To follow this automated, test-based validation approach, the specification of FLAME, written in Z, was translated into Prolog and 20,000 random tests were automatically generated and executed. Tests results helped to discover some inconsistencies not only in the formal specification, but also in the previous informal definitions of the analysis operations and in current analysis tools. After this process, the Prolog implementation of FLAME is being used as a reference implementation for some tool developers, some analysis operations have been formally specified for the first time with more generic semantics, and more VML s are being formally specified using FLAME.

Mapping the design-space of textual variability modeling languages: a refined analysis

Variability modeling is a major part of modern product line engineering. Graphical or table-based approaches to variability modeling are focused around abstract models and specialized tools to interact with these models. However, more recently textual variability modeling languages, comparable to some extent to programming languages, were introduced. We consider the recent trend in product line engineering towards textual variability modeling languages as a phenomenon, which deserves deeper analysis. In this article, we report on the results and approach of a literature survey combined with an expert study. In the literature survey, we identified 11 languages, which enable the textual specification of product line variability and which are sufficiently described for an in-depth analysis. We provide a classification scheme, useful to describe the range of capabilities of such languages. Initially, we identified the relevant capabilities of these languages from a literature survey. The result of this has been refined, validated and partially improved by the expert survey. A second recent phenomenon in product line variability modeling is the increasing scale of variability models. Some authors of textual variability modeling languages argue that these languages are more appropriate for large-scale models. As a consequence, we would expect specific capabilities addressing scalability in the languages. Thus, we compare the capabilities of textual variability modeling techniques, if compared to graphical variability modeling approaches and in particular to analyze their specialized capabilities for large-scale models.

A Case Study on a Quality Assurance Process for a Scientific Framework

In the quality assurance of scientific frameworks, both the special characteristics of scientific software and the large variability in frameworks must be accounted for. In previous research, the authors developed a process for handling the variability of a framework using software product line variability modeling. They described the design of a quality assurance process for scientific frameworks. This article reports on a case study analyzing the feasibility and acceptance by developers for two parts of the quality assurance process: variability model creation and desk-checking. The main results of the case study are that variability modeling is feasible for the development; however, a different variability modeling language is needed to be able to represent all important aspects. This study found acceptance of variability modeling and desk-checking.

A Study of Variability Models and Languages in the Systems Software Domain

Variability models represent the common and variable features of products in a product line. Since the introduction of FODA in 1990, several variability modeling languages have been proposed in academia and industry, followed by hundreds of research papers on variability models and modeling. However, little is known about the practical use of such languages. We study the constructs, semantics, usage, and associated tools of two variability modeling languages, Kconfig and CDL, which are independently developed outside academia and used in large and significant software projects. We analyze 128 variability models found in 12 open--source projects using these languages. Our study 1) supports variability modeling research with empirical data on the real-world use of its flagship concepts. However, we 2) also provide requirements for concepts and mechanisms that are not commonly considered in academic techniques, and 3) challenge assumptions about size and complexity of variability models made in academic papers. These results are of interest to researchers working on variability modeling and analysis techniques and to designers of tools, such as feature dependency checkers and interactive product configurators.