Supporting distributed product configuration by integrating heterogeneous variability modeling approaches

ContextIn industrial settings products are developed by more than one organization. Software vendors and suppliers commonly typically maintain their own product lines, which contribute to a larger (multi) product line or software ecosystem. It is unrealistic to assume that the participating organizations will agree on using a specific variability modeling technique—they will rather use different approaches and tools to manage the variability of their systems.ObjectiveWe aim to support product configuration in software ecosystems based on several variability models with different semantics that have been created using different notations.MethodWe present an integrative approach that provides a unified perspective to users configuring products in multi product line environments, regardless of the different modeling methods and tools used internally. We also present a technical infrastructure and a prototype implementation based on web services.ResultsWe show the feasibility of the approach and its implementation by using it with the three most widespread types of variability modeling approaches in the product line community, i.e., feature-based, OVM-style, and decision-oriented modeling. To demonstrate the feasibility and flexibility of our approach, we present an example derived from industrial experience in enterprise resource planning. We further applied the approach to support the configuration of privacy settings in the Android ecosystem based on multiple variability models. We also evaluated the performance of different model enactment strategies used in our approach.ConclusionsTools and techniques allowing stakeholders to handle variability in a uniform manner can considerably foster the initiation and growth of software ecosystems from the perspective of software reuse and configuration.     

Model-based incremental conformance checking to enable interactive product configuration

ContextModel-based product line engineering (PLE) is a paradigm that can enable automated product configuration of large-scale software systems, in which models are used as an abstract specification of commonalities and variabilities of products of a product line.ObjectiveIn the context of PLE, providing immediate feedback on the correctness of a manual configuration step to users has a practical impact on whether a configuration process with tool support can be successfully adopted in practice.MethodIn an existing work, a UML-based variability modeling methodology named as SimPL and an interactive configuration process was proposed. Based on the existing work, we propose an automated, incremental and efficient conformance checking approach to ensure that the manual configuration of a variation point conforms to a set of pre-defined conformance rules specified in the Object Constraint Language (OCL). The proposed approach, named as Zen-CC, has been implemented as an integrated part of our product configuration and derivation tool: Zen-Configurator.ResultsThe performance and scalability of Zen-CC have been evaluated with a real-world case study. Results show that Zen-CC significantly outperformed two baseline engines in terms of performance. Besides, the performance of Zen-CC remains stable during the configuration of all the 10 products of the product line and its efficiency also remains un-impacted even with the growing product complexity, which is not the case for both of the baseline engines.ConclusionThe results suggest that Zen-CC performs practically well and is much more scalable than the two baseline engines and is scalable for configuring products with a larger number of variation points.     

Facilitating the evolution of products in product line engineering by capturing and replaying configuration decisions

Software product lines rely on developing reusable artifacts and defining their variability in models to support and accelerate the derivation of individual products. A major challenge in product lines is the continuous evolution of both the reusable artifacts and derived products. Products that have been derived from a product line have to be updated regularly, e.g., after bugfixes or the development of new features. Changes to reusable artifacts and variability models have to be propagated to derived products. The aim of our research is to provide automated support for the evolution of products derived from product lines by capturing and replaying configuration decisions. Our PUPLE (Product Updates in Product Line Engineering) approach supports updating derived products after changes to the product line they have been derived from. It exploits the structure of variability models and uses change-tracking data to minimize user intervention. The paper first explores how different types of product line changes influence the derived products. It then presents extensions to our decision-oriented product line approach DOPLER to support product line evolution. We evaluate the feasibility of the PUPLE approach with evolution tasks that were performed by engineers of an industry partner on a product line of an Eclipse-based tool suite with six derived products. We conclude with lessons learned and limitations of our approach.     

Weaving variability into domain metamodels

Domain-specific modeling languages (DSMLs) are the essence of MDE. A DSML describes the concepts of a particular domain in a metamodel, as well as their relationships. Using a DSML, it is possible to describe a wide range of different models that often share a common base and vary on some parts. On the one hand, some current approaches tend to distinguish the variability language from the DSMLs themselves, implying greater learning curve for DSMLs stakeholders and a significant overhead in product line engineering. On the other hand, approaches integrating variability in DSMLs lack generality and tool support. We argue that aspect-oriented modeling techniques enabling flexible metamodel composition and results obtained by the software product line community to manage and resolve variability form the pillars for a solution for integrating variability into DSMLs. In this article, we consider variability as an independent and generic aspect to be woven into the DSML. In particular, we detail how variability is woven and how to perform product line derivation. We validate our approach through the weaving of variability into two different metamodels: Ecore??widely used for DSML definition??and SmartAdapters, our aspect model weaver. These results emphasize how new abilities of the language can be provided by this means.     

一种基于UML的软件产品线可变性建模方法

将UML引入到软件产品线开发中,在产品线可变性分析的基础上,提出了一种基于UML的产品线可变性建模方法。该方法不仅支持可选、多选一等可变点类型的描述,还支持软件产品线可变性的约束建模。在此基础上,还通过一个手机应用软件的产品线可变性建模实例验证了该方法的有效性。     

Simulating evolution in model-based product line engineering

ContextNumerous approaches are available for modeling product lines and their variability. However, the long-term impacts of model-based development on maintenance effort and model complexity can hardly be investigated due to a lack of empirical data. Conducting empirical research in product line engineering is difficult as companies are typically reluctant to provide access to data from their product lines. Also, many benefits of product lines can be measured only in longitudinal studies, which are difficult to perform in most environments.ObjectiveIn this paper, we thus aim to explore the benefit of simulation to investigate the evolution of model-based product lines.MethodWe present a simulation approach for exploring the effects of product line evolution on model complexity and maintenance effort. Our simulation considers characteristics of product lines (e.g., size, dependencies in models) and we experiment with different evolution profiles (e.g., technical refactoring vs. placement of new products).ResultsWe apply the approach in a simulation experiment that uses data from real-world product lines from the domain of industrial automation systems to demonstrate its feasibility.ConclusionOur results demonstrate that simulation contributes to understanding the effects of maintenance and evolution in model-based product lines.     

Issue-based variability management

ContextVariability management is a key activity in software product line engineering. This paper focuses on managing rationale information during the decision-making activities that arise during variability management. By decision-making we refer to systematic problem solving by considering and evaluating various alternatives. Rationale management is a branch of science that enables decision-making based on the argumentation of stakeholders while capturing the reasons and justifications behind these decisions.ObjectiveDecision-making should be supported to identify variability in domain engineering and to resolve variation points in application engineering. We capture the rationale behind variability management decisions. The captured rationale information is useful to evaluate future changes of variability models as well as to handle future instantiations of variation points. We claim that maintaining rationale will enhance the longevity of variability models. Furthermore, decisions should be performed using a formal communication between domain engineering and application engineering.MethodWe initiate the novel area of issue-based variability management (IVM) by extending variability management with rationale management. The key contributions of this paper are: (i) an issue-based variability management methodology (IVMM), which combines questions, options and criteria (QOC) and a specific variability approach; (ii) a meta-model for IVMM and a process for variability management and (iii) a tool for the methodology, which was developed by extending an open source rationale management tool.ResultsRationale approaches (e.g. questions, options and criteria) guide distributed stakeholders when selecting choices for instantiating variation points. Similarly, rationale approaches also aid the elicitation of variability and the evaluation of changes. The rationale captured within the decision-making process can be reused to perform future decisions on variability.ConclusionIVMM was evaluated comparatively based on an experimental survey, which provided evidence that IVMM is more effective than a variability modeling approach that does not use issues.     

Configuring use case models in product families

In many domains such as automotive and avionics, the size and complexity of software systems is quickly increasing. At the same time, many stakeholders tend to be involved in the development of such systems, which typically must also be configured for multiple customers with varying needs. Product Line Engineering (PLE) is therefore an inevitable practice for such systems. Furthermore, because in many areas requirements must be explicit and traceability to them is required by standards, use cases and domain models are common practice for requirements elicitation and analysis. In this paper, based on the above observations, we aim at supporting PLE in the context of use case-centric development. Therefore, we propose, apply, and assess a use case-driven configuration approach which interactively receives configuration decisions from the analysts to generate product-specific (PS) use case and domain models. Our approach provides the following: (1) a use case-centric product line modeling method (PUM), (2) automated, interactive configuration support based on PUM, and (3) an automatic generation of PS use case and domain models from Product Line (PL) models and configuration decisions. The approach is supported by a tool relying on Natural Language Processing (NLP) and integrated with an industrial requirements management tool, i.e., IBM DOORS. We successfully applied and evaluated our approach to an industrial case study in the automotive domain, thus showing evidence that the approach is practical and beneficial to capture variability at the appropriate level of granularity and to configure PS use case and domain models in industrial settings.     

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.     

Verification and analysis of domain-specific models of physical characteristics in embedded control software

ContextA considerable portion of the software systems today are adopted in the embedded control domain. Embedded control software deals with controlling a physical system, and as such models of physical characteristics become part of the embedded control software.ObjectiveDue to the evolution of system properties and increasing complexity, faults can be left undetected in these models of physical characteristics. Therefore, their accuracy must be verified at runtime. Traditional runtime verification techniques that are based on states/events in software execution are inadequate in this case. The behavior suggested by models of physical characteristics cannot be mapped to behavioral properties of software. Moreover, implementation in a general-purpose programming language makes these models hard to locate and verify. Therefore, this paper proposes a novel approach to perform runtime verification of models of physical characteristics in embedded control software.MethodThe development of an approach for runtime verification of models of physical characteristics and the application of the approach to two industrial case studies from the printing systems domain.ResultsThis paper presents a novel approach to specify models of physical characteristics using a domain-specific language, to define monitors that detect inconsistencies by exploiting redundancy in these models, and to realize these monitors using an aspect-oriented approach. We complement runtime verification with static analysis to verify the composition of domain-specific models with the control software written in a general-purpose language.ConclusionsThe presented approach enables runtime verification of implemented models of physical characteristics to detect inconsistencies in these models, as well as broken hardware components and wear and tear of hardware in the physical system. The application of declarative aspect-oriented techniques to realize runtime verification monitors increases modularity and provides the ability to statically verify this realization. The complementary static and runtime verification techniques increase the reliability of embedded control software.     

MOD2-SCM: A model-driven product line for software configuration management systems

ContextSoftware Configuration Management (SCM) is the discipline of controlling the evolution of large and complex software systems. Over the years many different SCM systems sharing similar concepts have been implemented from scratch. Since these concepts usually are hard-wired into the respective program code, reuse is hardly possible.ObjectiveOur objective is to create a model-driven product line for SCM systems. By explicitly describing the different concepts using models, reuse can be performed on the modeling level. Since models are executable, the need for manual programming is eliminated. Furthermore, by providing a library of loosely coupled modules, we intend to support flexible composition of SCM systems.MethodWe developed a method and a tool set for model-driven software product line engineering which we applied to the SCM domain. For domain analysis, we applied the FORM method, resulting in a layered feature model for SCM systems. Furthermore, we developed an executable object-oriented domain model which was annotated with features from the feature model. A specific SCM system is configured by selecting features from the feature model and elements of the domain model realizing these features.ResultsDue to the orthogonality of both feature model and domain model, a very large number of SCM systems may be configured. We tested our approach by creating instances of the product line which mimic wide-spread systems such as CVS, GIT, Mercurial, and Subversion.ConclusionThe experiences gained from this project demonstrate the feasibility of our approach to model-driven software product line engineering. Furthermore, our work advances the state of the art in the domain of SCM systems since it support the modular composition of SCM systems at the model rather than the code level.     

Structuring the modeling space and supporting evolution in software product line engineering

The scale and complexity of product lines means that it is practically infeasible to develop a single model of the entire system, regardless of the languages or notations used. The dynamic nature of real-world systems means that product line models need to evolve continuously to meet new customer requirements and to reflect changes of product line artifacts. To address these challenges, product line engineers need to apply different strategies for structuring the modeling space to ease the creation and maintenance of models. This paper presents an approach that aims at reducing the maintenance effort by organizing product lines as a set of interrelated model fragments defining the variability of particular parts of the system. We provide support to semi-automatically merge fragments into complete product line models. We also provide support to automatically detect inconsistencies between product line artifacts and the models representing these artifacts after changes. Furthermore, our approach supports the co-evolution of models and their respective meta-models. We discuss strategies for structuring the modeling space and show the usefulness of our approach using real-world examples from our ongoing industry collaboration.     

Model-driven generative development of measurement software

Metrics offer a practical approach to evaluate properties of domain-specific models. However, it is costly to develop and maintain measurement software for each domain-specific modeling language. In this paper, we present a model-driven and generative approach to measuring models. The approach is completely domain-independent and operationalized through a prototype that synthesizes a measurement infrastructure for a domain-specific modeling language. This model-driven measurement approach is model-driven from two viewpoints: (1) it measures models of a domain-specific modeling language; (2) it uses models as unique and consistent metric specifications, with respect to a metric specification metamodel which captures all the necessary concepts for model-driven specifications of metrics. The benefit from applying the approach is evaluated by four case studies. They indicate that this approach significantly eases the measurement activities of model-driven development processes.     

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.     

Operationalised product quality models and assessment: The Quamoco approach

ContextSoftware quality models provide either abstract quality characteristics or concrete quality measurements; there is no seamless integration of these two aspects. Quality assessment approaches are, hence, also very specific or remain abstract. Reasons for this include the complexity of quality and the various quality profiles in different domains which make it difficult to build operationalised quality models.ObjectiveIn the project Quamoco, we developed a comprehensive approach aimed at closing this gap.MethodThe project combined constructive research, which involved a broad range of quality experts from academia and industry in workshops, sprint work and reviews, with empirical studies. All deliverables within the project were peer-reviewed by two project members from a different area. Most deliverables were developed in two or three iterations and underwent an evaluation.ResultsWe contribute a comprehensive quality modelling and assessment approach: (1) A meta quality model defines the structure of operationalised quality models. It includes the concept of a product factor, which bridges the gap between concrete measurements and abstract quality aspects, and allows modularisation to create modules for specific domains. (2) A largely technology-independent base quality model reduces the effort and complexity of building quality models for specific domains. For Java and C# systems, we refined it with about 300 concrete product factors and 500 measures. (3) A concrete and comprehensive quality assessment approach makes use of the concepts in the meta-model. (4) An empirical evaluation of the above results using real-world software systems showed: (a) The assessment results using the base model largely match the expectations of experts for the corresponding systems. (b) The approach and models are well understood by practitioners and considered to be both consistent and well suited for getting an overall view on the quality of a software product. The validity of the base quality model could not be shown conclusively, however. (5) The extensive, open-source tool support is in a mature state. (6) The model for embedded software systems is a proof-of-concept for domain-specific quality models.ConclusionWe provide a broad basis for the development and application of quality models in industrial practice as well as a basis for further extension, validation and comparison with other approaches in research.     

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.     

Improving software product line configuration: A quality attribute-driven approach

ContextDuring the definition of software product lines (SPLs) it is necessary to choose the components that appropriately fulfil a product’s intended functionalities, including its quality requirements (i.e., security, performance, scalability). The selection of the appropriate set of assets from many possible combinations is usually done manually, turning this process into a complex, time-consuming, and error-prone task.ObjectiveOur main objective is to determine whether, with the use of modeling tools, we can simplify and automate the definition process of a SPL, improving the selection process of reusable assets.MethodWe developed a model-driven strategy based on the identification of critical points (sensitivity points) inside the SPL architecture. This strategy automatically selects the components that appropriately match the product’s functional and quality requirements. We validated our approach experimenting with different real configuration and derivation scenarios in a mobile healthcare SPL where we have worked during the last three years.ResultsThrough our SPL experiment, we established that our approach improved in nearly 98% the selection of reusable assets when compared with the unassisted analysis selection. However, using our approach there is an increment in the time required for the configuration corresponding to the learning curve of the proposed tools.ConclusionWe can conclude that our domain-specific modeling approach significantly improves the software architect’s decision making when selecting the most suitable combinations of reusable components in the context of a SPL.     

An architecture for automatically developing secure OLAP applications from models

ContextDecision makers query enterprise information stored in Data Warehouses (DW) by using tools (such as On-Line Analytical Processing (OLAP) tools) which use specific views or cubes from the corporate DW or Data Marts, based on the multidimensional modeling. Since the information managed is critical, security constraints have to be correctly established in order to avoid unauthorized accesses.ObjectiveIn previous work we have defined a Model-Driven based approach for developing a secure DWs repository by following a relational approach. Nevertheless, is also important to define security constraints in the metadata layer that connects the DWs repository with the OLAP tools, that is, over the same multidimensional structures that final users manage. This paper defines a proposal to develop secure OLAP applications and incorporates it into our previous approach.MethodOur proposal is composed of models and transformations. Our models have been defined using the extension capabilities from UML (conceptual model) and extending the OLAP package of CWM with security (logical model). Transformations have been defined by using a graphical notation and implemented into QVT and MOFScript. Finally, this proposal has been evaluated through case studies.ResultsA complete MDA architecture for developing secure OLAP applications. The main contributions of this paper are: improvement of a UML profile for conceptual modeling; definition of a logical metamodel for OLAP applications; and definition and implementation of transformations from conceptual to logical models, and from logical models to the secure implementation into a specific OLAP tool (SSAS).ConclusionOur proposal allows us to develop secure OLAP applications, providing a complete MDA architecture composed of several security models and automatic transformations towards the final secure implementation. Security aspects are early identified and fitted into a most robust solution that provides us a better information assurance and a saving of time in maintenance.     

Synthesizing interpreted domain-specific models to manage smart microgrids

The increase in prominence of model-driven software development (MDSD) has placed emphasis on the use of domain-specific modeling languages (DSMLs) during the development process. DSMLs allow for domain concepts to be conceptualized and represented at a high level of abstraction. Currently, most DSML models are converted into high-level languages (HLLs) through a series of model-to-model and/or model-to-text transformations before they are executed. An alternative approach for model execution is the interpretation of models directly without converting them into an HLL. These models are created using interpreted DSMLs (i-DSMLs) and realized using a semantic-rich execution engine or domain-specific virtual machine (DSVM).In this article we present an approach for model synthesis, the first stage of model interpretation, that separates the domain-specific knowledge (DSK) from the model of execution (MoE). Previous work on model synthesis tightly couples the DSK and MoE reducing the ability for implementations of the DSVM to be easily reused in other domains. To illustrate how our approach to model synthesis works for i-DSMLs, we have created MGridML, an i-DSML for energy management in smart microgrids, and an MGridVM prototype, the DSVM for MGridML. We evaluated our approach by performing experiments on the model synthesis aspect of MGridVM and comparing the results to a DSVM from the user-centric communication domain.