Mapping feature models onto domain models: ensuring consistency of configured domain models

We present an approach to model-driven software product line engineering which is based on feature models and domain models. A feature model describes both common and varying properties of the instances of a software product line. The domain model is composed of a structural model (package and class diagrams) and a behavioral model (story diagrams). Features are mapped onto the domain model by annotating elements of the domain model with features. An element of a domain model is specific to the features included in its feature annotation. An instance of the product line is defined by a set of selected features (a feature configuration). A configuration of the domain model is built by excluding all elements whose feature set is not included in the feature configuration. To ensure consistency of the configured domain model, we define constraints on the annotations of inter-dependent domain model elements. These constraints guarantee that a model element may be selected only when the model elements are also included on which it depends. Violations of dependency constraints may be removed automatically with the help of an error repair tool which propagates features to dependent model elements.     

Architectural evolution of FamiWare using cardinality-based feature models

ContextAmbient Intelligence systems domain is an outstanding example of modern systems that are in permanent evolution, as new devices, technologies or facilities are continuously appearing. This means it would be desirable to have a mechanism that helps with the propagation of evolution changes in deployed systems.ObjectiveWe present a software product line engineering process to manage the evolution of FamiWare, a family of middleware for ambient intelligence environments. This process drives the evolution of FamiWare middleware configurations using cardinality-based feature models, which are especially well suited to express the structural variability of ambient intelligence systems.MethodFamiWare uses cardinality-based feature models and clonable features to model the structural variability present in ambient intelligence systems, composed of a large variety of heterogeneous devices. Since the management evolution of configurations with clonable features is manually untreatable due to the high number of features, our process automates it and propagates changes made at feature level to the architectural components of the FamiWare middleware. This is a model driven development process as the evolution management, the propagation of evolution changes and the code generation are performed using some kind of model mappings and transformations. Concretely we present a variability modelling language to map the selection of features to the corresponding FamiWare middleware architectural components.ResultsOur process is able to manage the evolution of cardinality-based feature models with thousands of features, something which is not possible to tackle manually. Thanks to the use of the variability language and the automatic code generation it is possible to propagate and maintain a correspondence between the FamiWare architectural model and the code. The process is then able to calculate the architectural differences between the evolved configuration and the previous one. Checking these differences, our process helps to calculate the effort needed to perform the evolution changes in the customized products. To perform those tasks we have defined two operators, one to calculate the differences between two feature model configurations and another to create a new configuration from a previous one.ConclusionOur process automatically propagates the evolution changes of the middleware family into the existing configurations where the middleware is already deployed and also helps us to calculate the effort in performing the changes in every configuration. Finally, we validated our approach, demonstrating the functioning of the defined operators and showing that by using our tool we can generate evolved configurations for FamiWare with thousands of cloned features, for several case studies.     

Toward automated feature model configuration with optimizing non-functional requirements

ContextA software product line is a family of software systems that share some common features but also have significant variabilities. A feature model is a variability modeling artifact, which represents differences among software products with respect to the variability relationships among their features. Having a feature model along with a reference model developed in the domain engineering lifecycle, a concrete product of the family is derived by binding the variation points in the feature model (called configuration process) and by instantiating the reference model.ObjectiveIn this work we address the feature model configuration problem and propose a framework to automatically select suitable features that satisfy both the functional and non-functional preferences and constraints of stakeholders. Additionally, interdependencies between various non-functional properties are taken into account in the framework.MethodThe proposed framework combines Analytical Hierarchy Process (AHP) and Fuzzy Cognitive Maps (FCM) to compute the non-functional properties weights based on stakeholders’ preferences and interdependencies between non-functional properties. Afterwards, Hierarchical Task Network (HTN) planning is applied to find the optimal feature model configuration.ResultOur approach improves state-of-art of feature model configuration by considering positive or negative impacts of the features on non-functional properties, the stakeholders’ preferences, and non-functional interdependencies. The approach presented in this paper extends earlier work presented in [1] from several distinct perspectives including mechanisms handling interdependencies between non-functional properties, proposing a novel tooling architecture, and offering visualization and interaction techniques for representing functional and non-functional aspects of feature models.Conclusionour experiments show the scalability of our configuration approach when considering both functional and non-functional requirements of stakeholders.     

Scalable prediction of non-functional properties in software product lines: Footprint and memory consumption

ContextA software product line is a family of related software products, typically created from a set of common assets. Users select features to derive a product that fulfills their needs. Users often expect a product to have specific non-functional properties, such as a small footprint or a bounded response time. Because a product line may have an exponential number of products with respect to its features, it is usually not feasible to generate and measure non-functional properties for each possible product.ObjectiveOur overall goal is to derive optimal products with respect to non-functional requirements by showing customers which features must be selected.MethodWe propose an approach to predict a product’s non-functional properties based on the product’s feature selection. We aggregate the influence of each selected feature on a non-functional property to predict a product’s properties. We generate and measure a small set of products and, by comparing measurements, we approximate each feature’s influence on the non-functional property in question. As a research method, we conducted controlled experiments and evaluated prediction accuracy for the non-functional properties footprint and main-memory consumption. But, in principle, our approach is applicable for all quantifiable non-functional properties.ResultsWith nine software product lines, we demonstrate that our approach predicts the footprint with an average accuracy of 94%, and an accuracy of over 99% on average if feature interactions are known. In a further series of experiments, we predicted main memory consumption of six customizable programs and achieved an accuracy of 89% on average.ConclusionOur experiments suggest that, with only few measurements, it is possible to accurately predict non-functional properties of products of a product line. Furthermore, we show how already little domain knowledge can improve predictions and discuss trade-offs between accuracy and required number of measurements. With this technique, we provide a basis for many reasoning and product-derivation approaches.     

An algebraic semantics for MOF

In model-driven development, software artifacts are represented as models in order to improve productivity, quality, and cost effectiveness. In this area, the meta-object facility (MOF) standard plays a crucial role as a generic framework within which a wide range of modeling languages can be defined. The MOF standard aims at offering a good basis for model-driven development, providing some of the building concepts that are needed: what is a model, what is a metamodel, what is reflection in the MOF framework, and so on. However, most of these concepts are not yet fully formally defined in the current MOF standard. In this paper we define a reflective, algebraic, executable framework for precise metamodeling based on membership equational logic (mel) that supports the MOF standard. Our framework provides a formal semantics of the following notions: metamodel, model, and conformance of a model to its metamodel. Furthermore, by using the Maude language, which directly supports mel specifications, this formal semantics is executable. This executable semantics has been integrated within the Eclipse modeling framework as a plugin tool called MOMENT2. In this way, formal analyses, such as semantic consistency checks, model checking of invariants and LTL model checking, become available within Eclipse to provide formal support for model-driven development processes.     

Efficient synthesis of feature models

ContextVariability modeling, and in particular feature modeling, is a central element of model-driven software product line architectures. Such architectures often emerge from legacy code, but, creating feature models from large, legacy systems is a long and arduous task. We describe three synthesis scenarios that can benefit from the algorithms in this paper.ObjectiveThis paper addresses the problem of automatic synthesis of feature models from propositional constraints. We show that the decision version of the problem is NP-hard. We designed two efficient algorithms for synthesis of feature models from CNF and DNF formulas respectively.MethodWe performed an experimental evaluation of the algorithms against a binary decision diagram (BDD)-based approach and a formal concept analysis (FCA)-based approach using models derived from realistic models.ResultsOur evaluation shows a 10 to 1,000-fold performance improvement for our algorithms over the BDD-based approach. The performance of the DNF-based algorithm was similar to the FCA-based approach, with advantages for both techniques. We identified input properties that affect the runtimes of the CNF- and DNF-based algorithms.ConclusionsOur algorithms are the first known techniques that are efficient enough to be used on dependencies extracted from real systems, opening new possibilities of creating reverse engineering and model management tools for variability models.     

SimPL: A product-line modeling methodology for families of integrated control systems

ContextIntegrated control systems (ICSs) are heterogeneous systems where software and hardware components are integrated to control and monitor physical devices and processes. A family of ICSs share the same software code base, which is configured differently for each product to form a unique installation. Due to the complexity of ICSs and inadequate automation support, product configuration in this context is typically error-prone and costly.ObjectiveAs a first step to overcome these challenges, we propose a UML-based product-line modeling methodology that provides a foundation for semi-automated product configuration in the specific context of ICSs.MethodWe performed a comprehensive domain analysis to identify characteristics of ICS families, and their configuration challenges. Based on this, we formulated the characteristics of an adequate configuration solution, and derived from them a set of modeling requirements for a model-based solution to configuration. The SimPL methodology is proposed to fulfill these requirements.ResultsTo evaluate the ability of SimPL to fulfill the modeling requirements, we applied it to a large-scale industrial case study. Our experience with the case study shows that SimPL is adequate to provide a model of the product family that meets the modeling requirements. Further evaluation is still required to assess the applicability and scalability of SimPL in practice. Doing this requires conducting field studies with human subjects and is left for future work.ConclusionWe conclude that configuration in ICSs requires better automation support, and UML-based approaches to product family modeling can be tailored to provide the required foundation.     

A code tagging approach to software product line development

Software product line engineering seeks to systematise reuse when developing families of similar software systems so as to minimise development time, cost and defects. To realise variability at the code level, product line methods classically advocate usage of inheritance, components, frameworks, aspects or generative techniques. However, these might require unaffordable paradigm shifts for developers if the software was not thought at the outset as a product line. Furthermore, these techniques can be conflicting with a company’s coding practices or external regulations. These concerns were the motivation for the industry–university collaboration described in this paper in which we developed a minimally intrusive coding technique based on tags. The approach was complemented with traceability from code to feature diagrams which were exploited for automated configuration. It is supported by a toolchain and is now in use in the partner company for the development of flight-grade satellite communication software libraries.     

An Approach for Recovering Distributed System Architectures

Reasoning about software systems at the architectural level is key to effective software development, management, evolution and reuse. All too often, though, the lack of appropriate documentation leads to a situation where architectural design information has to be recovered directly from implemented software artifacts. This is a very demanding process, particularly when involving recovery of runtime abstractions (clients, servers, interaction protocols, etc.) that are typical to the design of distributed software systems. This paper presents an exploratory reverse engineering approach, called X-ray, to aid programmers in recovering architectural runtime information from a distributed system's existing software artifacts. X-ray comprises three domain-based static analysis techniques, namely component module classification, syntactic pattern matching, and structural reachability analysis. These complementary techniques can facilitate the task of identifying a distributed system's implemented executable components and their potential runtime interconnections. The component module classification technique automatically distinguishes source code modules according to the executables components they implement. The syntactic pattern matching technique in turn helps to recognise specific code fragments that may implement typical component interaction features. Finally, the structural reachability analysis technique aids in the association of those features to the code specific for each executable component. The paper describes and illustrates the main concepts underlying each technique, reports on their implementation as a suit of new and off-the-shelf tools, and, to give evidence of the utility of the approach, provides a detailed account of a successful application of the three techniques to help recover a static approximation of the runtime architecture for Field, a publicly-available distributed programming environment.     

Quality attribute modeling and quality aware product configuration in software product lines

In software product line engineering, the customers mostly concentrate on the functionalities of the target product during product configuration. The quality attributes of a target product, such as security and performance, are often assessed until the final product is generated. However, it might be very costly to fix the problem if it is found that the generated product cannot satisfy the customers’ quality requirements. Although the quality of a generated product will be affected by all the life cycles of product development, feature-based product configuration is the first stage where the estimation or prediction of the quality attributes should be considered. As we know, the key issue of predicting the quality attributes for a product configured from feature models is to measure the interdependencies between functional features and quality attributes. The current existing approaches have several limitations on this issue, such as requiring real products for the measurement or involving domain experts’ efforts. To overcome these limitations, we propose a systematic approach of modeling quality attributes in feature models based on domain experts’ judgments using the analytic hierarchical process (AHP) and conducting quality aware product configuration based on the captured quality knowledge. Domain experts’ judgments are adapted to avoid generating the real products for quality evaluation, and AHP is used to reduce domain experts’ efforts involved in the judgments. A prototype tool is developed to implement the concepts of the proposed approach, and a formal evaluation is carried out based on a large-scale case study.     

Type safety for feature-oriented product lines

A feature-oriented product line is a family of programs that share a common set of features. A feature implements a stakeholder’s requirement and represents a design decision or configuration option. When added to a program, a feature involves the introduction of new structures, such as classes and methods, and the refinement of existing ones, such as extending methods. A feature-oriented decomposition enables a generator to create an executable program by composing feature code solely on the basis of the feature selection of a user—no other information needed. A key challenge of product line engineering is to guarantee that only well-typed programs are generated. As the number of valid feature combinations grows combinatorially with the number of features, it is not feasible to type check all programs individually. The only feasible approach is to have a type system check the entire code base of the feature-oriented product line. We have developed such a type system on the basis of a formal model of a feature-oriented Java-like language. The type system guaranties type safety for feature-oriented product lines. That is, it ensures that every valid program of a well-typed product line is well-typed. Our formal model including type system is sound and complete.     

Aspect-oriented model-driven code generation: A systematic mapping study

ContextModel-driven code generation is being increasingly applied to enhance software development from perspectives of maintainability, extensibility and reusability. However, aspect-oriented code generation from models is an area that is currently underdeveloped.ObjectiveIn this study we provide a survey of existing research on aspect-oriented modeling and code generation to discover current work and identify needs for future research.MethodA systematic mapping study was performed to find relevant studies. Classification schemes have been defined and the 65 selected primary studies have been classified on the basis of research focus, contribution type and research type.ResultsThe papers of solution proposal research type are in a majority. All together aspect-oriented modeling appears being the most focused area divided into modeling notations and process (36%) and model composition and interaction management (26%). The majority of contributions are methods.ConclusionAspect-oriented modeling and composition mechanisms have been significantly discussed in existing literature while more research is needed in the area of model-driven code generation. Furthermore, we have observed that previous research has frequently focused on proposing solutions and thus there is need for research that validates and evaluates the existing proposals in order to provide firm foundations for aspect-oriented model-driven code generation.     

Assessing the maintainability of software product line feature models using structural metrics

A software product line is a unified representation of a set of conceptually similar software systems that share many common features and satisfy the requirements of a particular domain. Within the context of software product lines, feature models are tree-like structures that are widely used for modeling and representing the inherent commonality and variability of software product lines. Given the fact that many different software systems can be spawned from a single software product line, it can be anticipated that a low-quality design can ripple through to many spawned software systems. Therefore, the need for early indicators of external quality attributes is recognized in order to avoid the implications of defective and low-quality design during the late stages of production. In this paper, we propose a set of structural metrics for software product line feature models and theoretically validate them using valid measurement-theoretic principles. Further, we investigate through controlled experimentation whether these structural metrics can be good predictors (early indicators) of the three main subcharacteristics of maintainability: analyzability, changeability, and understandability. More specifically, a four-step analysis is conducted: (1) investigating whether feature model structural metrics are correlated with feature model maintainability through the employment of classical statistical correlation techniques; (2) understanding how well each of the structural metrics can serve as discriminatory references for maintainability; (3) identifying the sufficient set of structural metrics for evaluating each of the subcharacteristics of maintainability; and (4) evaluating how well different prediction models based on the proposed structural metrics can perform in indicating the maintainability of a feature model. Results obtained from the controlled experiment support the idea that useful prediction models can be built for the purpose of evaluating feature model maintainability using early structural metrics. Some of the structural metrics show significant correlation with the subjective perception of the subjects about the maintainability of the feature models.     

Dynamic decision models for staged software product line configuration

Software product line engineering practices offer desirable characteristics such as rapid product development, reduced time-to-market, and more affordable development costs as a result of systematic representation of the variabilities of a domain of discourse that leads to methodical reuse of software assets. The development lifecycle of a product line consists of two main phases: domain engineering, which deals with the understanding and formally modeling of the target domain, and application engineering that is concerned with the configuration of a product line into one concrete product based on the preferences and requirements of the stakeholders. The work presented in this paper focuses on the application engineering phase and builds both the theoretical and technological tools to assist the stakeholders in (a) understanding the complex interactions of the features of a product line; (b) eliciting the utility of each feature for the stakeholders and hence exposing the stakeholders’ otherwise implicit preferences in a way that they can more easily make decisions; and (c) dynamically building a decision model through interaction with the stakeholders and by considering the structural characteristics of software product line feature models, which will guide the stakeholders through the product configuration process. Initial exploratory empirical experiments that we have performed show that our proposed approach for helping stakeholders understand their feature preferences and its associated staged feature model configuration process is able to positively impact the quality of the end results of the application engineering process within the context of the limited number of participants. In addition, it has been observed that the offered tooling support is able to ease the staged feature model configuration process.     

Multi-purpose,multi-level feature modeling of large-scale industrial software systems

Feature models are frequently used to capture the knowledge about configurable software systems and product lines. However, feature modeling of large-scale systems is challenging as models are needed for diverse purposes. For instance, feature models can be used to reflect the perspectives of product management, technical solution architecture, or product configuration. Furthermore, models are required at different levels of granularity. Although numerous approaches and tools are available, it remains hard to define the purpose, scope, and granularity of feature models. This paper first reports results and experiences of an exploratory case study on developing feature models for two large-scale industrial automation software systems. We report results on the characteristics and modularity of the feature models, including metrics about model dependencies. Based on the findings from the study, we developed FORCE, a modeling language, and tool environment that extends an existing feature modeling approach to support models for different purposes and at multiple levels, including mappings to the code base. We demonstrate the expressiveness and extensibility of our approach by applying it to the well-known Pick and Place Unit example and an injection molding subsystem of an industrial product line. We further show how our approach supports consistency between different feature models. Our results and experiences show that considering the purpose and level of features is useful for modeling large-scale systems and that modeling dependencies between feature models is essential for developing a system-wide perspective.     

Model-driven engineering of middleware-based ubiquitous services

Supporting the execution of service-oriented applications over ubiquitous networks specifically calls for a service-oriented middleware (SOM), which effectively enables ubiquitous networking while benefiting from the diversity and richness of the networking infrastructure. However, developing ubiquitous applications that exploit the specific features offered by a SOM might be a time-consuming task, which demands a deep knowledge spanning from the application domain concepts down to the underlying middleware technicalities. In this paper, first we present the model-driven development process underpinning ubiSOAP, a SOM for the ubiquitous networking domain. Then, based on the domain concepts defined by the conceptual model of ubiSOAP, its architecture and its technicalities, we propose a domain-specific environment, called ubiDSE, that aids the development of applications that exploits the ubiSOAP features, from design to implementation. ubiDSE allows developers to focus on the main behavior of the modeled systems, rather than on complex details inherent to ubiquitous environments. As part of ubiDSE, specific tools are provided to automatically generate skeleton code for service-oriented applications to be executed on ubiSOAP-enabled devices, hence facilitating the exploitation of ubiSOAP by developers.     

A Component-Based Software Configuration Management Model and Its Supporting System

Software configuration management(SCM)is an important key technology in software development.Component-based software development (CBSD)is an emerging paradigm in software development.However,to apply CBSD effectively in real world practice,supporting SCM in CBSD needs to be further investigated.In this paper,the objects that need to be managed in CBSD is analyzed and a component-based SCM model is presented.In this model,Components,as the integral logical constituents in a system,are managed as the basic configuration items in SCM,and the relationships between/among components are defined and maintained.Based on this model.a configuration management system is implemented.     

Automated generation of test oracles using a model-driven approach

ContextSoftware development time has been reduced with new development tools and paradigms, testing must accompany these changes. In order to release software products in a timely manner as well as to minimise the impact of possible errors introduced during maintenance interventions, testing automation has become a central goal. Whilst research has produced significant results in test case generation and tools for test case (re)-execution, one of the most important open problems in testing is the automation of oracle generation. The oracle decides whether the program under test has or has not behaved correctly and then issues a pass/fail verdict. In most cases, writing the oracle is a time-consuming activity that, moreover, is manual in most cases.ObjectiveThis article automates two important steps in the test oracle: obtention of expected output and its comparison with the actual output, using a model-driven approach.MethodThe oracle automation problem is resolved using a model-driven framework, based on OMG standards: UML is used as metamodel and QVT and MOF2Text as transformation languages. The automated testing framework takes the models that describe the system as input, using UML notation and derives from them the test model and then the test code, following a model-driven approach. Test oracle procedures are obtained from a UML state machine.ResultsA complete executable test case at functional test level is obtained, composed of a test procedure with parametrized input test data and expected result automation.ConclusionThe oracle automation is obtained using a model-driven approach, test cases are obtained automatically from UML models. The model-driven testing framework was applied to an industrial application and has been useful to testing automation for the main functionalities in the system.