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  • 1. Farbe! Generative Software Engineering Summer Semester 2012 Prof. Dr. Bernhard Rumpe Software Engineering RWTH Aachen University http://www.se-rwth.de/
  • 2. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen Software EngineeringSeite 2 Prof. Dr. Bernhard Rumpe Department of Computer Science 3, Ahornstraße 55, 3rd floor Email rumpe @ se-rwth.de Consultation Tue, 13:45 – 14:15 (see also website) Martin Schindler Email schindler @ se-rwth.de Consultation on request via e-mail Pedram Mir Seyed Nazari Email nazari @ se-rwth.de Consultation on request via e-mail © Lehrstuhl für Software Engineering, RWTH Aachen
  • 3. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen Lecture Generative Software EngineeringSeite 3  Lecture (3+2) – 6 credits  Tuesday, 13:30 - 15:00, AH I  Thursday, 15:45 – 17:15, 5056  Lab class • Intermixed lectures and • exercises during the semester: homework + demonstration  Audience: • Computational Engineering Science (D) • Informatik (D)/Hauptstudium/Praktische Informatik • Werkstoffinformatik (D)/Hauptdiplom • Werkstoffinformatik (D)/Hauptdiplom/Softwaretechnik, Sprachen und Architekturen • Technik-Kommunikation (M.A.)/2. Hauptfach (Technisches Fach)/Grundlagen der Informatik/Hauptstudium/Spezialisierung Informatik • Informatik (GYM+GS,SII)/Hauptstudium/B. Praktische Informatik • Informatik (M.Sc.)/Software und Kommunikation • SSE (M.Sc.), /Software Engineering, /Core Subjects, SSE (M.Sc.)/[MPO2010] Software Engineering,
  • 4. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen Lecture Generative Software EngineeringSeite 4  Requirements: • Course Software Engineering • Good programming skills, ideally Java • Modeling skills, ideally UML  Helpful: • Compiler construction  Slides: • L2P: https://www2.elearning.rwth-aachen.de/ss12/12ss-23428  Mailinglist for up-to-date information about this lecture: • se-teaching@lists.rwth-aachen.de
  • 5. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen LiteratureSeite 5  UML (german) [Rum11] B. Rumpe: Modellierung mit UML: Sprache, Konzepte und Methodik. 2te Auflage, Springer 2011. [Rum12] B. Rumpe: Agile Modellierung mit UML: Code- generierung, Testfälle, Refactoring. 2te Auflage, Springer 2012.  Generation for product lines: [CE00] K. Czarnecki, U. Eisenecker: Generative Programming: Methods, Tools, and Applications. Addison-Wesley 2000  Slides are available as PDFs and up-to-date information about lecture and lab class on the website at least 24 hours prior to the beginning of the lecture.  The slides contain the shortened content of the lectures.
  • 6. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen More LiteratureSeite 6  [GSCK04] J. Greenfield et al. : Software Factories, Wiley 2004  [SV05] T. Stahl, M. Völter : Modellgetriebene Softwareentwicklung: Techniken, Engineering, Management. dpunkt 2005.  [MP10] M. Fowler, R. Parsons: Domain Specific Languages. Addison-Wesley, 2010.  http://en.wikipedia.org/wiki/Unified_Modeling_Language
  • 7. GenerativeSoftwareEngineering1. Introduction1.1. Problem StatementProf. Dr. Bernhard RumpeSoftware EngineeringRWTH Aachen Universityhttp://www.se-rwth.de/
  • 8. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen Problems of Software EngineeringSeite 8  Percentage of software in products is still increasing dramatically  Complexity increases by orders of magnitudes  Embedded software • video, mobiles, cars, planes, home automation  Typical problems in unsuccessful projects are • software is finalized too late • wrong functionality is implemented • software is not documented/commented • source code is missing • technical platform is changing • business model/requirements change © Lehrstuhl für Software Engineering, RWTH Aachen
  • 9. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen Increasing ComplexitySeite 9 1960 1970 1980 1990 2000 60 M O I 50 M O I SPAC E E SD f r W ü SH U TT LE BB -SD N I 40 M O I LUN AR 30 M O I M SS O N I I CO N TRO L 20 M O I E SD - P S W A APO LLO WM 4 . 2 7% äh r lche s j i i äs P r du k tv it t - o 10 M O I w a ch s t m u G EM N I I E SD - P S W A D BP - 4 1 M ER CU R Y M O I: illonen O b e k t ode - I s tr k tonen M i j c n u i E SD :E e k tr n sche sW äh sys t m D g it l W l o i l e i a Q ue len :B oeh 87 ,S . 5 und S e en s U n t ragen zu S e na r I du s tre le l m 4 im ( e l m m i n i l S o fw a r - e chn k ,D eu t che I f m a tk - kade e B onn 588 ) t eT i s nor i A m i Siemens EWSD V8.1: 12,5 million LOC, about 190.000 pages of documentation
  • 10. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen Trends in Software DevelopmentSeite 10  Size and complexity of systems continually increase: • Isolated solutions  company-wide integration  E-Commerce  Systems-Of-Systems  World-Wide Cyber-Infrastructure  New technologies: • EJB, XML, .Net, Cloud, ...  Diversification of application domains: • Embedded systems, business systems, telecommunication, mobility, ad-hoc changing infrastructures  Growing methodological experience how to deal with challenges • Agile Methods, e.g, address unstable requirements, time-to-market pressure, lean and effective development for small projects • Improved analytical techniques Portfolio of software development processes / techniques etc.
  • 11. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen Our GoalsSeite 11 Define, improve, and industrially apply  techniques, concepts, and methods  for innovative and efficient development of  software and software-intensive systems,  such that high-quality products can be developed  in a shorter period of time and  with flexible integration of changing requirements. (SE mission statement)
  • 12. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen Portfolio of Software EngineeringSeite 12  Goal: Extension of the portfolio of techniques, concepts, methods, and tools  such that for every problem the adequate techniques for every developer is provided.
  • 13. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen Elements of the PortfolioSeite 13  Examples: • Concepts: Hierarchical Decomposition („Divide Et Impera“), Modularization, Encapsulation, Components • Techniques: State based / Functional / OO … Development • Tools: Compiler, GIT/SVN, Eclipse • Methods: CRC-Cards for Requirement Elicitation, Reviews, Test procedures, Extreme Programming, V-Modell, RUP • Languages: for documentation, implementation, or modeling © Lehrstuhl für Software Engineering, RWTH Aachen
  • 14. GenerativeSoftwareEngineering1. Introduction1.2. Languages for ModelingProf. Dr. Bernhard RumpeSoftware EngineeringRWTH Aachen Universityhttp://www.se-rwth.de/
  • 15. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen Computer LanguagesSeite 15  Languages are a key for any computer science • Modeling languages like UML • Programming languages • English for documentation  Any automation needs a precisely defined language to deal with. Bibtex Prolog Fortran Basic UML Java Perl Cobol Lisp Scala PHP Statecharts Ada ABAP Groovy Matlab Simulink SQL C# Delphi ML C JavaScript Haskell D Scheme VHDL Lustre Verilog Logo Eiffel Forth C++
  • 16. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen Unified Modeling Language UMLSeite 16 (Source: Wikipedia)  UML is a second generation notation for object oriented modeling  Organized by the OMG (Object Management Group)
  • 17. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen Structural Diagrams of the UMLSeite 17 class diagram composition structure diagram package diagram component diagram object diagram deployment diagram
  • 18. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen Behavioral Diagrams of the UMLSeite 18 use case diagram sequence diagram timing diagram activity diagram interaction overview communication diagram diagram statechart + textual part: Object Constraint Language (OCL)
  • 19. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen Domain Specific Languages (DSL)Seite 19  Domain-Specific Languages allow us to abstractly model application domains like • cars, business workflows, telecommunication, production systems, construction of railway tracks, ...  Example: Railway track control which uses • rails, sensors, switches, signals Sensor Sensor • A framework provides a software component for every hardware element. • The configuration is defined by a graphical DSL
  • 20. Prof. Dr. B. RumpeLehrstuhl fürSoftware Engineering Domain Specific Languages (DSL):RWTH AachenSeite 20 More Examples  Autofilter [RVWL03] • determining the position of satellites • configuration and selection of Kalman-Filters • automatic code generation • production code • proof that behavior is optimal  Matlab/Simulink (a rather general “DSL”) • diagrams from control theory • automatic code generation for different platforms
  • 21. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen Graphical vs. Textual RepresentationSeite 21  Different representations: • Graphical vs. Textual  Which one is more compact?  Better to assess? To Overview?  Easier to read/write? A C C = A; D = !A && B; E = !A && !B; ¬ - Æ D B Æ E ¬ - © Lehrstuhl für Software Engineering, RWTH Aachen
  • 22. Prof. Dr. B. RumpeLehrstuhl fürSoftware Engineering Domain Specific Languages (DSL):RWTH AachenSeite 22 MontiArc  Textual ADL for hierarchical modeling event-based asynchronous communication systems: components, ports and channels  Component has multiple I/O-ports  Channels connect ports unidirectional  Evaluation/simulation of modeled systems: • code generator: event-based, timed simulation • asynchronous message passing • message flows modeled with timed streams  (based on MontiCore-Tooling Infrastructure)
  • 23. Prof. Dr. B. RumpeLehrstuhl fürSoftware Engineering Domain Specific Languages (DSL):RWTH AachenSeite 23 MontiArc – Textual Syntax MA InteriorLightArbiter component definition SwitchStatus LightCtrl OnOffCmd cmd DoorStatus DoorEval BlinkRequest OnOffRequest component AlarmStatus AlarmCheck InteriorLightArbiter{ ac autoconnect port; port ports in SwitchStatus, in DoorStatus, in AlarmStatus, hierarchical out OnOffCmd cmd; containment connectors component LightCtrl; component DoorEval; component AlarmCheck ac; connect LightCtrl.OnOffCmd -> cmd; }
  • 24. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen Definition: Domain-Specific LanguagesSeite 24 "A small, usually declarative language expressive over the distinguishing characteristics of a set of programs in a particular problem domain." [DK98] "A DSL is a language designed to be useful for a limited set of tasks, ...." [JB06]  A DSL is a language • which focuses more on the application domain than the technical solution, • not necessarily executable (if it is executable, it is often not Turing complete), • and allows describing / solving problems from an application domain in an efficient way.  DSML = DSL for Modeling (of Software)
  • 25. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen Goals of this LectureSeite 25 1. Understand what modeling in SE means 2. Understand how to use generative tools 3. Understand how a generator works 4. Understand how a generator is adapted to specific needs 5. Understand how to write a generator for a DSL
  • 26. GenerativeSoftwareEngineering1. Introduction1.3. Methodological considerationsProf. Dr. Bernhard RumpeSoftware EngineeringRWTH Aachen Universityhttp://www.se-rwth.de/
  • 27. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen DSL-driven DevelopmentSeite 27  Domain Specific Modeling Languages (DSML) as a central notation in the development process static analysis documentation rapid prototyping DSML models refactoring/ transformation automated tests code generation • DSMLs serve as central notation for development of software • a DSML can be programming, test, or modeling language
  • 28. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen Principle of Code GenerationSeite 28  Basically: mapping the model into code model generated code generator platform specific code, frameworks, operating system ... hardware Generator maps models to code on top of predefined frameworks. Problem 1: not all needed code can/should be generated. Problem 2: generated code is platform specific
  • 29. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen Code Generator: Parameterization + Runtime SystemSeite 29 This is the principal structure of the generated code allowing parameterization, manual coding, and using a runtime system manually written code Predefined Predefined Predefined components components model components API API Parame- terized generated code generator + included parts runtime system Environment: hardware, GUI, frameworks Generator script/template Map: concept code
  • 30. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen Roles in a Generative ProjectSeite 30 application component programmer provider application manually modeler Predefined written code Predefined Predefined components components model components API API Parame- terized generated code generator + included parts runtime system Environment: hardware, GUI, frameworks Generator script/template Map: concept code tool provider generator customizer (“toolsmith”) © Lehrstuhl für Software Engineering, RWTH Aachen
  • 31. Prof. Dr. B. RumpeLehrstuhl fürSoftware Engineering Alternative:RWTH AachenSeite 31 Model used for Customization / Configuration  E.g., we get a model like that  used for customization of a generic product: GUI Workflows Data base System Protocols  e.g., Data Management Systems, SAP, etc.  Use of models at runtime: Dynamic loading and interpretation instead of code generation.
  • 32. Prof. Dr. B. RumpeLehrstuhl fürSoftware Engineering Constructive use of Models for Coding and Testing:RWTH AachenSeite 32 Usage of UML-Diagrams statecharts object class diagrams deployment diagrams C++, sequence __: __: diagram Java … diagrams __: OCL consistency parameterized test code analyser code generator generator „smells“ & tests system errors see: B. Rumpe: Agile Modellierung mit UML, Springer Verlag 2011
  • 33. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen Model-based Simulation for SESeite 33  Test-Infrastructure needs simulation of its context:  context can be: geographical, sociological, etc.  Simulation helps to understand complexity object class statecharts C++, deployment diagrams diagrams Java … __: __: diagram __: sequence OCL diagrams DSLs DSLs system tests
  • 34. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen Core Elements of an Agile Modeling MethodSeite 34  Incremental modeling  Modeling tests  Automatic analysis: Types, dataflow, control flow, ...  Code generation for system and tests from compact models  Small increments  Intensive simulation with customer participation for feedback  Refactoring for incremental extension and optimization  Common ownership of models  ... This approach uses elements of agile methods based on the UML notation
  • 35. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen How the Approach supports Agile DevelopmentSeite 35 Core characteristics of agility: Improvement through use of models: Efficiency + increased through advanced of the developers notation & tools Reactivity: + incremental, small cycles flexibility to deal with changes + model-based refactoring Customer focus + rapid feedback Rely on individuals + less tedious work - skilled people are necessary Simplicity + simplifies refactoring / evolution Quality + automated tests is an emerging property + better review-able designs + common ownership & pairwise development of models © Lehrstuhl für Software Engineering, RWTH Aachen
  • 36. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen UML vs. a specific DSLSeite 36  UML advantages: • language is known, less starting effort • tools are available (at least front-ends)  DSL advantages: • more appropriate for the domain • more power in expressing concepts, thus better to use • domain expert can understand, model, and optimize it (?)  In general: • UML is easier to start with; a well defined DSL is more powerful and productive in the long run  Tool/DSL vendors task: • provide powerful and easy to use and customize DSL- infrastructure, enabling agile use of DSLs
  • 37. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen Challenges of DSLsSeite 37  Additional effort of a DSL • design, implementation, and maintenance • training of the developers  Language design is difficult • scope • level of abstraction • syntax total costs Conventional model based SW engineering Break-Even DSL approach initial investment lifetime of the system ?
  • 38. Prof. Dr. B. RumpeLehrstuhl fürSoftware Engineering DSL-based Increase of Productivity:RWTH AachenSeite 38 Empirical Evidence total costs Conventional Break-Even software engineering DSL approach Initial investment lifetime of the system ?  Productivity gain 3:1 • Software Component Generation vs. Ada Templates [KMB96]  Productivity gain 10:1 • Nokia Case Study (MetaCase)  Generated LOC vs. handcoded DSL-code at least 15:1 • model traversal and transformations [GK03]
  • 39. GenerativeSoftwareEngineering1. Introduction1.4. Generative Software EngineeringProf. Dr. Bernhard RumpeLehrstuhl Informatik 3 (Software Engineering)RWTH Aachenhttp://www.se-rwth.de/
  • 40. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen Generative Software EngineeringSeite 40  Generative software engineering (GSE) is a • Method that uses generators to efficiently generate software systems or parts of software systems from models written in UML or a DSL in order to increase quality and decrease development time.  If DSLs are used, domain experts can model, understand, validate, and optimize the software system directly.  UML models or DSLs are used to model certain aspects of a software system in an intuitive and concise manner.  Of-the-shelf or hand-made generators process the models to generate production and test code.
  • 41. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen GSE uses Modeling Technologies: OMG MDASeite 41  OMG: www.omg.org • Object Management Group • Standardization organization • Most famous standards • UML • Corba • BPM  MDA • Model Driven Architecture  Drawbacks • Restricted to UML-models • Fixed levels of abstraction • Synchronization and transition between views hinders effective agile development
  • 42. Prof. Dr. B. RumpeLehrstuhl fürSoftware Engineering Views inRWTH AachenSeite 42 Model Driven Architecture  Computation Independent Model (CIM) • business- and domainview of the system Computation Independent  Platform Independent Model (PIM) Model (CIM) • abstract, without details about DESIGN middleware etc. Platform  Platform Specific Model (PSM) Independent Model (PIM) • technology-specific, optimizations for certain platforms TRANSFORMATIONS Platform Platform Specific Model Specific Model (PSM) (PSM)
  • 43. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen Idealized View on Model Driven ArchitectureSeite 43 Requirement use cases and scenarios: sequence diagrams describe users viewpoint application classes define data structures state machines describe states and behavior T T T T technical class diagram T adaptation, extension, technical design + behavior for technical classes T T T code generation + integration with manually written code system complete and running system
  • 44. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen Problems of Model Driven ArchitectureSeite 44 Requirement • No reuse • Tool chain too long • No efficient tools • Tracing problems • Evolution is awkward T T T • Lot of information missing, e.g., T - design rationale T - non-functional reqs. T T T • “Agile” development is not possible • SE-Models are not integrated with other Engineering Models (spatial, biological, ...) system
  • 45. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen Generative Programming [CE00] uses DSLsSeite 45  Generative Programming • "Software engineering paradigm based on modeling software system families such that, given a particular requirements specification, a highly customized and optimized intermediate or end-product can be automatically manufactured on demand from elementary, reusable implementation components by means of configuration knowledge." [CE00, p. 5] Car Tachometer Navigation Cell Phone Adapter Ventilation Air Conditioning +
  • 46. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen References (1)Seite 46 [CE00] K. Czarnecki, U. Eisenecker: Generative Programming: Methods, Tools, and Applications. Addison-Wesley 2000 [DK98] A. v. Deursen, P. Klint: Little Languages: Little Maintenance? J. of Software Maintenance: Research and Practice, 1998 [GK03] J. Gray, G. Karsai: An Examination of DSLs for Concisely Representing Model Traversals and Transformations Proceedings of HICSS, 2003, 325 [GSCK04] J. Greenfield et al. : Software Factories, Wiley 2004 [HGK+08] J. Hartmann; H. Grönniger; H. Krahn; S. Kriebel; L. Rothhardt, B. Rumpe: Modelling Automotive Function Nets with Views for Features, Variants, and Modes. Proceedings of 4th European Congress ERTS 2008 [JB06] Frederic Jouault & Jean Bezivin KM3: a DSL for Metamodel Specification Proceedings of 8th IFIP International Conference on Formal Methods for Open Object-Based Distributed Systems (LNCS 4037), 2006, 171-185 [KMB96] Richard B. Kieburtz; Laura McKinney; Jeffrey M. Bell; James Hook; Alex Kotov; Jeffrey Lewis; Dino P. Oliva; Tim Sheard; Ira Smith & Lisa Walton: A software engineering experiment in software component generation. ICSE 96: Proceedings of the 18th international conference on Software engineering, IEEE Computer Society, 1996, 542-552
  • 47. Prof. Dr. B. RumpeLehrstuhl fürSoftware EngineeringRWTH Aachen References (2)Seite 47 [Rum11] B. Rumpe: Modellierung mit UML: Sprache, Konzepte und Methodik. 2te Auflage, Springer 2011. [Rum12] B. Rumpe: Agile Modellierung mit UML: Codegenerierung, Testfälle, Refactoring. 2te Auflage, Springer 2012. [RVWL03] G. Rosu; R. Venkatesan; J. Whittle & L. Leustean Certifying optimality of state estimation programs Proc. of CAV03 (LNCS 2725), 2003, 301-314 [SV05] T. Stahl, Markus Völter : Modellgetriebene Softwareentwicklung: Techniken, Engineering, Mangement. dpunkt 2005. © Lehrstuhl für Software Engineering, RWTH Aachen

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