53) Feature Models, Domain Models and Product Lines

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1 Fakultät Informatik, Institut für Software- und Multimediatechnik, Lehrstuhl für Softwaretechnologie 53) Feature Models, Domain Models and Product Lines 1. Feature Models 2. Product Linie Configuration with Feature Models 3. Multi-Stage Configuration Prof. Dr. U. Aßmann Florian Heidenreich Technische Universität Dresden Institut für Software- und Multimediatechnik Gruppe Softwaretechnologie Version , 30. Januar 2011 Obligatory Literature Florian Heidenreich, Jan Kopcsek, and Christian Wende. FeatureMapper: Mapping Features to Models. In Companion Proceedings of the 30th International Conference on Software Engineering (ICSE'08), Leipzig, Germany, May Models.pdf Prof. U. Aßmann Feature-driven SPLE 2

2 References [Aßm03] U. Aßmann. Invasive Software Composition. Springer, [Cza05] K. Czarnecki and M. Antkiewicz. Mapping Features to Models: A Template Approach Based on Superimposed Variants. In R. Glück and M. Lowry, editors, Proceedings of the 4th International Conference on Generative Programming and Component Engineering (GPCE'05), volume 3676 of LNCS, pages Springer, [Cza06] K. Czarnecki and K. Pietroszek. Verifying Feature-Based Model Templates Against Well-Formedness OCL Constraints. In Proceedings of the 5th International Conference on Generative Programming and Component Engineering (GPCE'06), pages , New York, NY, USA, ACM. [Hei08a] F. Heidenreich, J. Kopcsek, and C. Wende. FeatureMapper: Mapping Features to Models. In Companion Proceedings of the 30th International Conference on Software Engineering (ICSE'08), pages , New York, NY, USA, May ACM. [Hei08b] Florian Heidenreich, Ilie Şavga and Christian Wende. On Controlled Visualisations in Software Product Line Engineering. In Proc. of the 2nd Int l Workshop on Visualisation in Software Product Line Engineering (ViSPLE 2008), collocated with the 12th Int l Software Product Line Conference (SPLC 2008), Limerick, Ireland, September [Hei09] Florian Heidenreich. Towards Systematic Ensuring Well-Formedness of Software Product Lines. In Proceedings of the 1st Workshop on Feature- Oriented Software Development (FOSD 2009) collocated with MODELS/GPCE/ SLE Denver, Colorado, USA, October ACM Press Prof. U. Aßmann Feature-driven SPLE Slide 3 Object-Oriented Analysis vs Object-Oriented Design requirements specification textual requirements (stories) context analysis use cases domain analysis architectural design detailed design Prof. U. Aßmann Feature-driven SPLE 4

3 Extended to Model-Driven Architecture (MDA) requirements specification textual requirements (stories) use cases context analysis (CIM) analysis domain Platform independent Platform-1 specific Platform-(1,.., n) specific Prof. U. Aßmann Feature-driven SPLE 5 Product Lines (Product Families) requirements specification Feature textual Model requirements (varability (stories) Product 1 context ) analysis Model use cases domain Product 2 Product n Prof. U. Aßmann Feature-driven SPLE 6

4 Adding Extensions to Abstract Models in the MDA In the following, we extend the MDA (below) with configuration Platform independent (PIM) Platform-1-specific extension (PSE) Model weaving Platform-1 specific (PIM) Platform-2 specific extension (PSE) Model weaving Platform-(1+2) specific (PSM) Prof. U. Aßmann Feature-driven SPLE 7 Configuration of Variabilities in Product Lines Als Oberle des Analysels: Analyseklassen erben von Domänenklassen Domain Model Configuration With FeatureModel Variants Product Variants Design Variants Model weaving Analysis Model Product Line Model (Framework, VIM) Configuration With FeatureModel Variants PSM Variants Extensions Product PIM Model weaving Product Product PSM PSM Product PSM Prof. U. Aßmann Feature-driven SPLE 8

5 53.1 PRODUCT LINES WITH FEATURE TREES AND FEATURE MODELS Prof. U. Aßmann Feature-driven SPLE 9 Feature Models for Product configuration Feature s are used to express variability in Product Lines alternative, mandatory, optional features, and their relations A variant represents a concrete product from the product line The variant results from a selection of a subgraph of the feature The variant can be used to parameterize and drive the product instantiation process Prof. U. Aßmann Feature-driven SPLE 10

6 Feature Models Feature Tree Notation Group of AND Features Group of Alternative (XOR) Features Mandatory Feature Optional Feature Group of OR Features FeatureA FeatureB FeatureC FeatureD PhD Thesis, Czarnecki (1998) based on FODA-Notation by Kang et al. (1990) Prof. U. Aßmann Feature-driven SPLE 11 Beispiel A1 or A2 or A3 B1; B2 xor B3 B4; optional B5 B1; B7 A1 A2 A3 B1 B2 B3 B4 B5 B6 B7 Prof. U. Aßmann Feature-driven SPLE 12

7 [K. Lehmann, Diplomarbeit] FM Ein Featurel für computergestützte kognitive Reha K J E S CE CU NO NM... ge gz IS IT T A IS IT ge gz T A ge gz IS IT T A ge gz IS IT T A [1-2] [1-2] [1-2] [1-2] A/K K J E S CE CU NO NM... [c1] ge T A IS IT IS IT [c1] T A [c1] ge gz IS IT T A [1-2] [1-2] [1-2] [1-2] [c1] IS IT T A Prof. U. Aßmann Feature-driven SPLE 13 Mapping Features to Model Fragments (Model Snippets) Bridging the gap between configuration and solution space Need for mapping of features from feature s to artefacts of the solution space Possible artefacts Models defined in DSLs Model fragments (snippets) Architectural artefacts (components, connectors, aspects) Source code Files But how can we achieve the mapping...? Prof. U. Aßmann Feature-driven SPLE 14

8 Ex: Plugins have Features (in Eclipse) Prof. U. Aßmann Feature-driven SPLE 15 Prof. U. Aßmann 53.2 PRODUCT-LINE CONFIGURATION WITH FEATURE MODELS Feature-driven SPLE 16

9 Different Approaches of Variant Selection Additive approach Map all features to fragments ( snippets) Compose them with a core based on the presence of the feature in the variant Pros: conflicting variants can be led correctly strong per-feature decomposition Cons: traceability problems increased overhead in linking the different fragments Prof. U. Aßmann Feature-driven SPLE 17 Different Approaches of Variant Selection (2) Subtractive approach Model all features in one Remove elements based on absence of the feature in the variant Pros: no need for redundant links between artifacts short cognitive distance Cons: conflicting variants can't be led correctly huge and inconcise s Prof. U. Aßmann Feature-driven SPLE 18

10 The Mapping Problem between Features and Solution Elements Problem Space Solu5on Space FeatureA Creation A B Visualisation FeatureB FeatureC FeatureD FeatureE Validation Derivation F G E D Prof. U. Aßmann Feature-driven SPLE Slide 19 Mapping Features to Models FeatureMapper - a tool for mapping of feature s to ling artefacts developed at the ST Group Screencast and paper available at Advantages: Explicit representation of mappings Configuration of large product lines from selection of variants in feature trees Customers understand Consistency of each product in the line is simple to check Model and code snippets can be traced to requirements Prof. U. Aßmann Feature-driven SPLE 20

11 FeatureMapper Prof. U. Aßmann Slide 21 Feature-driven SPLE Mapping Features to Models We chose an explicit Mapping Representation in our tool FeatureMapper Mappings are stored in a mapping that is based on a mapping meta Feature Model Mapping Model Solu5on Models FeatureA FeatureC D A B FeatureC E FeatureB FeatureC FeatureD FeatureE FeatureE FeatureC FeatureE G F F G E D Prof. U. Aßmann Feature-driven SPLE Slide 22

12 From Feature Mappings to Model Transformations Prof. U. Aßmann Feature-driven SPLE 23 Visualisation of Mappings (1) Visualisations play a crucial role in Software Engineering It s hard to impossible to understand a complex system unless you look at it from different points of view In many cases, developers are interested only in a particular aspect of the connection between a feature and realising artefacts How a particular feature is realised? Which features communicate or interact in their realisation? Which artefacts may be effectively used in a variant? Solution of the FeatureMapper: MappingViews, a visualisation technique that provides four basic visualisations Realisation View Variant View Context View Property-Changes View Prof. U. Aßmann Feature-driven SPLE Slide 24

13 Realisation View For one Variant Model, the realisation in the solution space is shown Feature Model Mapping System Rela5onship Rela5onship FeatureB Rela5onship Prof. U. Aßmann Feature-driven SPLE Slide 25 Variant View The variant view shows different realisations in parallel Feature Model Mapping System Rela5onship Rela5onship Address Rela5onship Address Address Prof. U. Aßmann Feature-driven SPLE Slide 26

14 Context View The Context View draws the variants with different colors Aspect-separation: each variant forms an aspect Feature Model Mapping System Rela5onship Rela5onship... Address Rela5onship Group Group Group... Address Address... Prof. U. Aßmann Feature-driven SPLE Slide 27 Property-Changes View Feature Model System Arbitrary Depth Mapping Recorded change- set of changing the cardinality of the reflexive associa5on of Group to itself from 1 to many Other Feature Arbitrary Depth Prof. U. Aßmann Feature-driven SPLE Slide 28

15 Textual Languages Support (1) Unified handling of ling languages and textual languages by lifting textual languages to the ling level with the help of EMFText All >80 languages from the EMFText Syntax Zoo are supported, including Java 5 Prof. U. Aßmann Feature-driven SPLE Slide 29 Textual Languages Support (2) Aspect-related color markup of the code Prof. U. Aßmann Feature-driven SPLE Slide 30

16 Mapping-based Derivation of Transformations Transformations in the solution space build the product Mapping Model Feature Model Solu5on Models FeatureA FeatureC D A B FeatureC E FeatureB FeatureC FeatureE FeatureC G F F G E D FeatureD FeatureE FeatureE Variant Model <<in>> <<in>> Variant FeatureA A B FeatureB FeatureC <<in>> Derivation Of Transformations <<out>> E D FeatureD Prof. U. Aßmann Feature-driven SPLE Slide MULTI-STAGE CONFIGURATION Prof. U. Aßmann Feature-driven SPLE 32

17 FEASIPLE: A Multi-Stage Process Architecture for PLE Chose one variant on each level Feature Tree as input for the configuration of the weavings Prof. U. Aßmann Feature-driven SPLE 33 FEASIPLE: A Multi-Stage Process Architecture for PLE Goal: a staged MDSD-framework for PLE where each stage produces the software artefacts used for the next stage Prof. U. Aßmann Feature-driven SPLE 34

18 Advantages of FEASIPLE Characteristic feature 1: Variability on each stage Prof. U. Aßmann Feature-driven SPLE 35 Advantages of FEASIPLE Characteristic feature 2: Different ling languages, component systems and composition languages per stage Prof. U. Aßmann Feature-driven SPLE 36

19 Advantages of FEASIPLE Characteristic feature 3: Different composition mechanisms per stage Prof. U. Aßmann Feature-driven SPLE 37 Advantages of FEASIPLE Characteristic feature 4: Composition mechanisms are driven by variant selection Prof. U. Aßmann Feature-driven SPLE 38

20 Multi-Staged Derivation of Transformations How do we compose transformations? Between different stages? func)onal Feature Model VIM Mapping Variant Independent Model M2M Trafos context Feature Model PIM Mapping Platform Independent Models Platform Independent Models PlaLorm Independent Models M2M Trafos pla-orm Feature Model PSM Mapping Platform Specific Models Platform Specific Models PlaLorm Specific Models M2C Generators PlaLorm Specific Code Prof. U. Aßmann Feature-driven SPLE Folie 39 TraCo: A Framework for Safe Multi-Stage Composition of Transformations TraCo encapsulates transformations into composable components Arranges them with composition programs of parallel and sequential transformation steps (multi-threaded transformation SA1 Functional variant V1 M1 T1 SA2 SA1* Platform variant V2 M2 T2 T1* M1* V1* SA3 SA2* V1 Feature Selection M1 Mapping SA1 Solution Artefact T1 Transformation SAn-1 Context variant Vn Mn Tn SAn Prof. U. Aßmann Feature-driven SPLE Folie 40

21 Steps in Multi-Staged Derivation of Transformations 1. Transformations are represented as composable components 2. Definition and Composition of Transformation Steps A Composition System is needed (course CBSE): Allows for reuse of arbitrary existing transformation techniques 3. Validation of each transformation and composition step Type-checking Invariant- and constraint-checking Correctness of port and parameter binding Static and dynamic analysis 4. Execution of composition program Component instances Component Adapter references Actual Transformation Code Connectors Constant value Prof. U. Aßmann Feature-driven SPLE Slide 41 Multi-Staged Derivation of Transformations Implemented in our tool TraCo Component Model, Composition Language, Composition Technique Prof. U. Aßmann Feature-driven SPLE Slide 42

22 Composition Programs can be Configured (Metacomposition) Anything you can do, I do meta (Charles Simonyi) The composition program shown in the last slide can be subject to transformation and composition If we build a product line with TraCo, platform variability can be realised by different transformation steps A TraCo composition program can be used with FeatureMapper Multi-Staged transformation steps Even of composition programs More about metacomposition in CBSE course Prof. U. Aßmann Feature-driven SPLE Folie 43 The final frontier: Ensuring Well-formedness of SPLs Motivation: Make sure that well-formedness of all participating s is ensured Feature Model Mapping Model Solution Models Well-formedness rules are described using OCL Constraints are enforced during mapping time Prof. U. Aßmann Feature-driven SPLE Slide 44

23 Case Studies with FeatureMapper, TraCo, and FEASIPLE Simple Contact Management Application Software Product Line FeatureMapper used to map features to UML2 elements Both static and dynamic ling Simple Time Sheet Application Software Product Line FeatureMapper used to tailor ISC composition programs ISC used as a universal variability mechanism in SPLE Meta Transformation SalesScenario Software Product Line FeatureMapper used to tailor s expressed in Ecore-based DSLs was developed in project feasiple ( TAOSD AOM Crisis Management System Prof. U. Aßmann Feature-driven SPLE Slide 45 Summary Configuration of product lines with mapping of feature s to solution spaces Mapping of Features to s in Ecore-based languages using FeatureMapper Visualisations of those mappings using MappingViews Realisation View Variant View Context View Property-Changes View Derivation of solution s based on variant selection and mapping Multi-Staged derivation using TraCo Ensuring well-formedness of SPLs Prof. U. Aßmann Feature-driven SPLE Slide 46

24 Prof. U. Aßmann Feature-driven SPLE 47 The End