Modeling Business Process Variability

This master thesis presents research findings on business process variability modeling. Its main goal is to analyze inherent problems of business process variability and solve them simply, innovatively and effectively. To achieve this goal, process variability is defined by analyzing scientific literature, its main problems identified and is illustrated using a healthcare running example: process variability is classified into process variability within the domain space and over time. These two forms of process variability respectively lead to process variability modeling and process model evolution problems. After defining the main problems inherent to process variability, the focus of this research project is defined: solving process variability modeling problems. First current business process modeling languages are evaluated to assess the effectiveness of their respective modeling concepts when modeling process variability, using a newly created set of evaluation criteria and the healthcare running example. The following business process modeling languages are evaluated: Event driven process chains (EPC), the Business Process Modeling Notation (BPMN) and Configurable EPC (C-EPC). Business process variability modeling and Software product line engineering have similar problems. Therefore the variability modeling concepts developed by software product line engineering are analyzed. Feature diagrams and software configuration management are the main variability management concepts provided by software product line engineering. To apply these variability management concepts to model process variability meant combining them with existing business modeling languages. Riebisch feature diagrams are combined with C-EPC to form Feature-EPC. Applying software configuration management, meant merging Change Oriented Versioning with basic EPC to create COV-EPC, and merging the Proteus Configuration Language with basic EPC to design PCL-EPC. Finally these newly created business process modeling languages are also evaluated using the newly designed evaluation criteria and the healthcare running example. EPC or BPMN are not suited to model business process variability within the domain space. C-EPC provide explicit means to model business process variability, however the process models tend to get big very fast. Furthermore the syntax, the contextual constraints and the semantics of the configuration requirements and guidelines used to configure the C-EPC process models are unclear. Feature-EPC improve C-EPC with domain modeling capability and clearly defined configuration rules: their syntax, contextual constraints and semantics have been clearly defined using a context free grammar in Backus-Naur form. Furthermore, consistent combinations of features and configuration rules are ensured using respectively constraints and a conflict resolution algorithm. However, Feature-EPC and C-EPC suffer from the same weakness: large configurable process models. In COV-EPC and PCL-EPC the problem of large configurable process models is solved. COV-EPC ensures consistent combinations of options and configuration rules using respectively validities and a conflict resolution algorithm. PCL-EPC guarantees consistent combinations of process fragments by means of a PCL specification

Towards Anomaly Explanation in Feature Models

Feature models are a wide-spread approach to variability and commonality management in software product lines. Due to the increasing size and complexity of feature models, anomalies in terms of inconsistencies and redundancies can occur which lead to increased efforts related to feature model development and maintenance. In this paper we introduce knowledge representations which serve as a basis for the explanation of anomalies in feature models. On the basis of these representations we show how explanation algorithms can be applied. The results of a performance analysis show the applicability of these algorithms for anomaly detection in feature models. We conclude the paper with a discussion of future research issues

Automated Analysis of Orthogonal Variability Models Using Constraint Programming.

Software Product Line (SPL) Engineering is about producing a family of products that share commonalities and variabilities. The variability models are used for variability management in SPLs. Currently, the automated analysis of variability models has become an active research area. in this paper we focus on the automated analysis of Orthogonal Variability Model (OVM), which is a modelling language for representing variability. The automated analysis of OVMs deals with the computer-aided extraction of information from OVMs. The automated analysis of OVMs has been hardly explored and currently has no tooling support. Considering our know-how to analyse feature models, which are the most popular variability models in SPLs, we propose to automate the analysis of OVMs by means of constraint programming. in addition, we propose to extend OVMs with attributes, allowing to add extra-functional information to OVMs. With this proposal we contribute with a step forward toward a tooling support for analysing OVMs

Automated analysis of feature models 20 years later: a literature review

Software product line engineering is about producing a set of related products that share more commonalities than variabilities. Feature models are widely used for variability and commonality management in software product lines. Feature models are information models where a set of products are represented as a set of features in a single model. The automated analysis of feature models deals with the computer–aided extraction of information from feature models. The literature on this topic has contributed with a set of operations, techniques, tools and empirical results which have not been surveyed until now. This paper provides a comprehensive literature review on the automated analysis of feature models 20 years after of their invention. This paper contributes by bringing together previously-disparate streams of work to help shed light on this thriving area. We also present a conceptual framework to understand the different proposals as well as categorise future contributions. We finally discuss the different studies and propose some challenges to be faced in the future.CICYT TIN2009-07366CICYT TIN2006-00472Junta de Andalucía TIC-253

Probabilistic estimates of future changes in California temperature and precipitation usingstatistical and dynamical downscaling

Sixteen global general circulation models were used to develop probabilistic projections of temperature (T) and precipitation (P) changes over California by the 2060s. The global models were downscaled with two statistical techniques and three nested dynamical regional climate models, although not all global models were downscaled with all techniques. Both monthly and daily timescale changes in T and P are addressed, the latter being important for a range of applications in energy use, water management, and agriculture. The T changes tend to agree more across downscaling techniques than the P changes. Year-to-year natural internal climate variability is roughly of similar magnitude to the projected T changes. In the monthly average, July temperatures shift enough that that the hottest July found in any simulation over the historical period becomes a modestly cool July in the future period. Januarys as cold as any found in the historical period are still found in the 2060s, but the median and maximum monthly average temperatures increase notably. Annual and seasonal P changes are small compared to interannual or intermodel variability. However, the annual change is composed of seasonally varying changes that are themselves much larger, but tend to cancel in the annual mean. Winters show modestly wetter conditions in the North of the state, while spring and autumn show less precipitation. The dynamical downscaling techniques project increasing precipitation in the Southeastern part of the state, which is influenced by the North American monsoon, a feature that is not captured by the statistical downscaling

PACOGEN : Automatic Generation of Pairwise Test Configurations from Feature Models

International audienceFeature models are commonly used to specify variability in software product lines. Several tools support feature models for variability management at different steps in the development process. However, tool support for test configuration generation is currently limited. This test generation task consists in systematically selecting a set of configurations that represent a relevant sample of the variability space and that can be used to test the product line. In this paper we propose PACOGEN to analyze feature models and automatically generate a set of configurations that cover all pairwise interactions between features. PACOGEN relies on constraint programming to generate configurations that satisfy all constraints imposed by the feature model and to minimize the set of the tests configurations. This work also proposes an extensive experiment, based on the state-of-the art SPLOT feature models repository, showing that PACOGEN scales over variability spaces with millions of configurations and covers pairwise with less configurations than other available tools

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

Automated Variability Analysis and Testing of an E-Commerce Site. An Experience Report

In this paper, we report on our experience on the development of La Hilandera, an e-commerce site selling haberdashery products and craft supplies in Europe. The store has a huge input space where customers can place almost three millions of different orders which made testing an ex-tremely di cult task. To address the challenge, we explored the applicability of some of the practices for variability management in software product lines. First, we used a feature model to represent the store input space which provided us with a variability view easy to understand, share and discuss with all the stakeholders. Second, we used techniques for the automated analysis of feature models for the detection and repair of inconsistent and missing con guration settings. Finally, we used test selection and prioritization techniques for the generation of a manageable and effective set of test cases. Our ndings, summarized in a set of lessons learnt, suggest that variability techniques could successfully address many of the challenges found when developing e-commerce sites.CICYT TIN2012-32273Junta de Andalucía TIC-5906Junta de Andalucía P12-TIC- 186

PLiMoS, a DSML to Reify Semantics Relationships: An Application to Model-Based Product Lines

In the Model-Based Product Line Engineering (MBPLE) context, modularization and separation of concerns have been introduced to master the inherent complexity of current developments. With the aim to exploit e ciently the variabilities and commonalities in MBPLs, the challenge of management of dependencies becomes essential (e.g. hierarchical and variability decomposition, inter-dependencies between models). However, one may observe that, in existing approaches, relational information (i) is mixed with other concerns, and (ii) lacks semantics and abstraction level identi cation. To tackle this issue, we make explicit the relationships and their semantics, and separate the relational concern into a Domain Speci c Modeling Language (DSML) called PLiMoS. Relationships are treated as rst-class entities and quali ed by operational semantics properties, organized into viewpoints to address distinct objectives, e.g. product derivation, variability consistency management, archi- tectural organization. This paper provides a description of the PLiMoS relationships de nition and its implementation in a model-based product line process using two variability languages: Feature Model and OVM. The independence with variability and core assets modeling languages provides bene ts to cope with the product line maintenance