Integrated Management of Variability in Space and Time in Software Families

Software Product Lines (SPLs) and Software Ecosystems (SECOs) are approaches to capturing families of closely related software systems in terms of common and variable functionality (variability in space). SPLs and especially SECOs are subject to software evolution to adapt to new or changed requirements resulting in different versions of the software family and its variable assets (variability in time). Both dimensions may be interconnected (e.g., through version incompatibilities) and, thus, have to be handled simultaneously as not all customers upgrade their respective products immediately or completely. However, there currently is no integrated approach allowing variant derivation of features in different version combinations. In this thesis, remedy is provided in the form of an integrated approach making contributions in three areas: (1) As variability model, Hyper-Feature Models (HFMs) and a version-aware constraint language are introduced to conceptually capture variability in time as features and feature versions. (2) As variability realization mechanism, delta modeling is extended for variability in time, and a language creation infrastructure is provided to devise suitable delta languages. (3) For the variant derivation procedure, an automatic version selection mechanism is presented as well as a procedure to derive large parts of the application order for delta modules from the structure of the HFM. The presented integrated approach enables derivation of concrete software systems from an SPL or a SECO where both features and feature versions may be configured.:I. Context and Preliminaries 1. The Configurable TurtleBot Driver as Running Example 1.1. TurtleBot: A Domestic Service Robot 1.2. Configurable Driver Functionality 1.3. Software Realization Artifacts 1.4. Development History of the Driver Software 2. Families of Variable Software Systems 2.1. Variability 2.1.1. Variability in Space and Time 2.1.2. Internal and External Variability 2.2. Manifestations of Configuration Knowledge 2.2.1. Variability Models 2.2.2. Variability Realization Mechanisms 2.2.3. Variability in Realization Assets 2.3. Types of Software Families 2.3.1. Software Product Lines 2.3.2. Software Ecosystems 2.3.3. Comparison of Software Product Lines and Software Ecosystems 3. Fundamental Approaches and Technologies of the Thesis 3.1. Model-Driven Software Development 3.1.1. Metamodeling Levels 3.1.2. Utilizing Models in Generative Approaches 3.1.3. Representation of Languages using Metamodels 3.1.4. Changing the Model-Representation of Artifacts 3.1.5. Suitability of Model-Driven Software Development 3.2. Fundamental Variability Management Techniques of the Thesis 3.2.1. Feature Models as Variability Models 3.2.2. Delta Modeling as Variability Realization Mechanism 3.2.3. Variant Derivation Process of Delta Modeling with Feature Models 3.3. Constraint Satisfaction Problems 3.4. Scope 3.4.1. Problem Statement 3.4.2. Requirements 3.4.3. Assumptions and Boundaries II. Integrated Management of Variability in Space and Time 4. Capturing Variability in Space and Time with Hyper-Feature Models 4.1. Feature Models Cannot Capture Variability in Time 4.2. Formal Definition of Feature Models 4.3. Definition of Hyper-Feature Models 4.4. Creation of Hyper-Feature Model Versions 4.5. Version-Aware Constraints to Represent Version Dependencies and Incompatibilities 4.6. Hyper-Feature Models are a True Extension to Feature Models 4.7. Case Study 4.8. Demarcation from Related Work 4.9. Chapter Summary 5. Creating Delta Languages Suitable for Variability in Space and Time 5.1. Current Delta Languages are not Suitable for Variability in Time 5.2. Software Fault Trees as Example of a Source Language 5.3. Evolution Delta Modules as Manifestation of Variability in Time 5.4. Automating Delta Language Generation 5.4.1. Standard Delta Operations Realize Usual Functionality 5.4.2. Custom Delta Operations Realize Specialized Functionality 5.5. Delta Language Creation Infrastructure 5.5.1. The Common Base Delta Language Provides Shared Functionality for all Delta Languages 5.5.2. Delta Dialects Define Delta Operations for Custom Delta Languages 5.5.3. Custom Delta Languages Enable Variability in Source Languages 5.6. Case Study 5.7. Demarcation from Related Work 5.8. Chapter Summary 6. Deriving Variants with Variability in Space and Time 6.1. Variant Derivation Cannot Handle Variability in Time 6.2. Associating Features and Feature Versions with Delta Modules 6.3. Automatically Select Versions to Ease Configuration 6.4. Application Order and Implicitly Required Delta Modules 6.4.1. Determining Relevant Delta Modules 6.4.2. Forming a Dependency Graph of Delta Modules 6.4.3. Performing a Topological Sorting of Delta Modules 6.5. Generating Variants with Versions of Variable Assets 6.6. Case Study 6.7. Demarcation from Related Work 6.8. Chapter Summary III. Realization and Application 7. Realization as Tool Suite DeltaEcore 7.1. Creating Delta Languages 7.1.1. Shared Base Metamodel 7.1.2. Common Base Delta Language 7.1.3. Delta Dialects 7.2. Specifying a Software Family with Variability in Space and Time 7.2.1. Hyper-Feature Models 7.2.2. Version-Aware Constraints 7.2.3. Delta Modules 7.2.4. Application-Order Constraints 7.2.5. Mapping Models 7.3. Deriving Variants 7.3.1. Creating a Configuration 7.3.2. Collecting Delta Modules 7.3.3. Ordering Delta Modules 7.3.4. Applying Delta Modules 8. Evaluation 8.1. Configurable TurtleBot Driver Software 8.1.1. Variability in Space 8.1.2. Variability in Time 8.1.3. Integrated Management of Variability in Space and Time 8.2. Metamodel Family for Role-Based Modeling and Programming Languages 8.2.1. Variability in Space 8.2.2. Variability in Time 8.2.3. Integrated Management of Variability in Space and Time 8.3. A Software Product Line of Feature Modeling Notations and Constraint Languages 8.3.1. Variability in Space 8.3.2. Variability in Time 8.3.3. Integrated Management of Variability in Space and Time 8.4. Results and Discussion 8.4.1. Results and Discussion of RQ1: Variability Model 8.4.2. Results and Discussion of RQ2: Variability Realization Mechanism 8.4.3. Results and Discussion of RQ3: Variant Derivation Procedure 9. Conclusion 9.1. Discussion 9.1.1. Supported Evolutionary Changes 9.1.2. Conceptual Representation of Variability in Time 9.1.3. Perception of Versions as Incremental 9.1.4. Version Numbering Schemes 9.1.5. Created Delta Languages 9.1.6. Scalability of Approach 9.2. Possible Future Application Areas 9.2.1. Extend to Full Software Ecosystem Feature Model 9.2.2. Model Software Ecosystems 9.2.3. Extract Hyper-Feature Model Versions and Record Delta Modules 9.2.4. Introduce Metaevolution Delta Modules 9.2.5. Support Incremental Reconfiguration 9.2.6. Apply for Evolution Analysis and Planning 9.2.7. Enable Evolution of Variable Safety-Critical Systems 9.3. Contribution 9.3.1. Individual Contributions 9.3.2. Handling Updater Stereotypes IV. Appendix A. Delta Operation Generation Algorithm B. Delta Dialects B.1. Delta Dialect for Java B.2. Delta Dialect for Eclipse Projects B.3. Delta Dialect for DocBook Markup B.4. Delta Dialect for Software Fault Trees B.5. Delta Dialect for Component Fault Diagrams B.6. Delta Dialect for Checklists B.7. Delta Dialect for the Goal Structuring Notation B.8. Delta Dialect for EMF Ecore B.9. Delta Dialect for EMFText Concrete Syntax File

Meeting the design challenges of nano-CMOS electronics: an introduction to an upcoming EPSRC pilot project

The years of ‘happy scaling’ are over and the fundamental challenges that the semiconductor industry faces, at both technology and device level, will impinge deeply upon the design of future integrated circuits and systems. This paper provides an introduction to these challenges and gives an overview of the Grid infrastructure that will be developed as part of a recently funded EPSRC pilot project to address them, and we hope, which will revolutionise the electronics design industry

Climate Resilient & Equitable Water Systems Capital Scan

Climate change is affecting water supply, water management and the health of communities in U.S. cities. Changes in the timing, frequency and intensity of precipitation are placing stress on the built and natural systems that provide fresh water, manage storm water, and treat wastewater. Droughts are shrinking the water supply; heavy rainfall overburdens storm water systems, causing flooding in homes and neighborhoods. Low-income people and communities of color are often the most vulnerable to climate change, living in low-lying areas and lacking the resources to adapt and cope with challenges associated with these patterns.The cumulative impact of climate change on water resources not only leads to a reduction in water quality and the destruction of homes and property, but it can also be a threat to public health, force relocation of communities and cause economic harm.The vision of Kresge's Environment Program is to help communities build resilience in the face of climate change. We believe that cities are central to action on climate change and equity must be a fundamental part of our work in climate adaptation, climate mitigation and building social cohesion

Using numerical plant models and phenotypic correlation space to design achievable ideotypes

Numerical plant models can predict the outcome of plant traits modifications resulting from genetic variations, on plant performance, by simulating physiological processes and their interaction with the environment. Optimization methods complement those models to design ideotypes, i.e. ideal values of a set of plant traits resulting in optimal adaptation for given combinations of environment and management, mainly through the maximization of a performance criteria (e.g. yield, light interception). As use of simulation models gains momentum in plant breeding, numerical experiments must be carefully engineered to provide accurate and attainable results, rooting them in biological reality. Here, we propose a multi-objective optimization formulation that includes a metric of performance, returned by the numerical model, and a metric of feasibility, accounting for correlations between traits based on field observations. We applied this approach to two contrasting models: a process-based crop model of sunflower and a functional-structural plant model of apple trees. In both cases, the method successfully characterized key plant traits and identified a continuum of optimal solutions, ranging from the most feasible to the most efficient. The present study thus provides successful proof of concept for this enhanced modeling approach, which identified paths for desirable trait modification, including direction and intensity.Comment: 25 pages, 5 figures, 2017, Plant, Cell and Environmen

Technologies for climate change adaptation: agricultural sector

This Guidebook presents a selection of technologies for climate change adaptation in the agricultural sector. A set of twenty two adaptation technologies are showcased that are primarily based on the principals of agroecology, but also include scientific technologies of climate and biological sciences complemented with important sociological and institutional capacity building processes that are required to make adaptation function. The technologies cover monitoring and forecasting the climate, sustainable water use and management, soil management, sustainable crop management, seed conservation, sustainable forest management and sustainable livestock management. Technologies that tend to homogenize the natural environment and agricultural production have low possibilities of success in conditions of environmental stress that are likely to result from climate change. On the other hand, technologies that allow for, and indeed promote, diversity are more likely to provide a strategy which strengthens agricultural production in the face of uncertain future climate change scenarios. In this sense, the twenty two technologies showcased in this Guidebook have been selected because they facilitate the conservation and restoration of diversity while at the same time providing opportunities for increasing agricultural productivity. Many of these technologies are not new to agricultural production practices, but they are implemented based on assessment of current and possible future impacts of climate change in a particular location. Agro-ecology is an approach that encompasses concepts of sustainable production and biodiversity promotion and therefore provides a useful framework for identifying and selecting appropriate adaptation technologies for the agricultural sector. The Guidebook provides a systematic analysis of the most relevant information available on climate change adaptation technologies in the agriculture sector. It has been compiled based on a literature review of key publications, journal articles, and e-platforms, and by drawing on documented experiences sourced from a range of organizations working on projects and programmes concerned with climate change adaptation technologies in the agricultural sector. Its geographic scope is focused on developing countries where high levels of poverty, agricultural production, climate variability and biological diversity currently intersect. Key concepts around climate change adaptation are not universally agreed. It is therefore important to understand local contexts – especially social and cultural norms - when working with national and sub-national stakeholders to make informed decisions about appropriate technology options. Thus, decision-making processes should be participative, facilitated, and consensus-building oriented and should be based on the following key guiding principles: increasing awareness and knowledge, strengthening institutions, protecting natural resources, providing financial assistance and developing context-specific strategies. For decision-making the Community–Based Adaptation framework is proposed for creating inclusive governance that engages a range of stakeholders directly with local or district government and national coordinating bodies, and facilitates participatory planning, monitoring and implementation of adaptation activities. Seven criteria are suggested for the prioritization of adaptation technologies: (i) The extent to which the technology maintains or strengthens biological diversity and is environmentally sustainable; (ii) The extent to which the technology facilitates access to information systems and awareness of climate change information; (iii) Whether the technology support water, carbon and nutrient cycles and enables stable and/or increased productivity; (iv) Income-generating potential, cost-benefit analysis and contribution to improved equity; (v) Respect for cultural diversity and facilitation of inter-cultural exchange; (vi) Potential for integration into regional and national policies and can be scaled-up; (vii) The extent to which the technology builds formal and information institutions and social networks. Finally, recommendations are set out for practitioners and policy makers: • There is an urgent need for improved climate modelling and forecasting which can provide a basis for informed decision-making and the implementation of adaptation strategies. This should include traditional knowledge. • Information is also required to better understand the behaviour of plants, animals, pests and diseases as they react to climate change. • Potential changes in economic and social systems in the future under different climate scenarios should also be investigated so that the implications of adaptation strategy and planning choices are better understood. • It is important to secure effective flows of information through appropriate dissemination channels. This is vital for building adaptive capacity and decision-making processes. • Improved analysis of adaptation technologies is required to show how they can contribute to building adaptive capacity and resilience in the agricultural sector. This information needs to be compiled and disseminated for a range of stakeholders from local to national level. • Relationships between policy makers, researchers and communities should be built so that technologies and planning processes are developed in partnership, responding to producers’ needs and integrating their knowledge

Simple identification tools in FishBase

Simple identification tools for fish species were included in the FishBase information system from its inception. Early tools made use of the relational model and characters like fin ray meristics. Soon pictures and drawings were added as a further help, similar to a field guide. Later came the computerization of existing dichotomous keys, again in combination with pictures and other information, and the ability to restrict possible species by country, area, or taxonomic group. Today, www.FishBase.org offers four different ways to identify species. This paper describes these tools with their advantages and disadvantages, and suggests various options for further development. It explores the possibility of a holistic and integrated computeraided strategy

On the structure of problem variability: From feature diagrams to problem frames

Requirements for product families are expressed in terms of commonality and variability. This distinction allows early identification of an appropriate software architecture and opportunities for software reuse. Feature diagrams provide intuitive notations and techniques for representing requirements in product line development. In this paper, we observe that feature diagrams tend to obfuscate three important descriptions: requirements, domain properties and specifications. As a result, feature diagrams do not adequately capture the problem structures that underlie variability, and inform the solution structures of their complexity. With its emphasis on separation of the three descriptions, the problem frames approach provides a conceptual framework for a more detailed analysis of variability and its structure. With illustrations from an example, we demonstrate how problem frames analysis of variability can augment feature diagrams