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ART-SCENE: Modelling Complex Design Trade-off Spaces with i*

ART-SCENE: Modelling Complex Design Trade-off Spaces with i*. Neil Maiden Head of Centre. Overview. Our Centre and our research Who and what are we ART-SCENE A research prototype for scenario-based requirements engineering and trade-off decision-making

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ART-SCENE: Modelling Complex Design Trade-off Spaces with i*

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  1. ART-SCENE: Modelling Complex Design Trade-off Spaces with i* Neil Maiden Head of Centre

  2. Overview • Our Centre and our research • Who and what are we • ART-SCENE • A research prototype for scenario-based requirements engineering and trade-off decision-making • A pattern language for submarine manoeuvring • Research questions and approach • A pattern language modelled using i* formalism • Research questions revisited • Future research to evaluate ART-SCENE • Pattern language is baseline for scalable systems engineering research

  3. The Centre and its Environment • University Research Centre • Independent department (www-hcid.soi.city.ac.uk) in City’s School of Informatics (www.soi.city.ac.uk) • Objectives • Undertake world-class basic and applied research into the design of complex systems of which people are a significant component • Staff/students • Six academic, 7 research staff, 10 PhD students, 1 administrator and 1 visiting fellow • Research income • £1.6m income from 11 new research projects started since January 2000

  4. SIMP Basics and Partners • Systems Integration for Major Projects • Part of EPSRC’s Systems Integration Initiative • August 2000-August 2003, £800,000, 15 person-years • Academic partners • City: Neil Maiden and 2 research staff • UMIST: Alistair Sutcliffe and 2 research staff • QMW: Norman Fenton and 1 research staff • Industrial partners • BAE SYSTEMS: Very complex systems of systems • Kennedy-Carter: Allan Kennedy • Intellectual Capital Services: Philip M’Pherson • Produce general outcomes for systems engineering

  5. Our Research Objective • Deliver new decision-making capabilities • Make requirement-architecture trade-offs through scenario analyses • Analyse platform behaviour with different architectures through scenario analyses to determine requirement compliance • Improve on ad-hoc modelling and decision-making capabilities when engineering large systems • Key research question • Is it possible to assess conformance of different platform/equipment configurations to whole-system requirements in the context of different operational scenarios using a pattern-driven approach • Informed by innovative theories of knowledge reuse from analogical reasoning research?

  6. Start by Defining Patterns • SIMP defines a pattern as • A reusable architecture (the solution) to a collection of interconnected requirements (the problem) in well-defined scenarios (the context) • The architecture rationale in terms of decisions made to trade-off requirements to select the best-fit architecture solution • The effectiveness of the solution architecture in the defined scenarios, expressed as measurablelevels of requirements compliance • Therefore what we need is • Semantics for expressing the elements of the problem, solution and context of a pattern, interconnections between these elements, and formal constraints on constituent knowledge of a pattern

  7. Defining Pattern Languages • SIMP defines a pattern language as • A collection of patterns that, together, constitute an organisation’s systems engineering knowledge for a defined domain • The space of possible architecture solutions that have been considered to meet interconnected requirements, and trade-offs in that space • Associations between patterns in the pattern language, expressed as associations between different elements in 2 or more patterns • Facts and rules about requirements, architectures and scenarios in the defined domain • A glossary of terms about the elements of requirements, architectures and scenarios in the defined domain

  8. ART-SCENE • Research software prototype • Analysing Requirements Trade-offs with Scenario-driven Evaluations • Developed to investigate our key research question • ART-SCENE is a partial prototype • Implement using Microsoft VISIO2000, Visual Basic, Access, Visual InterDev, and Excel • Integrate with software tools for requirements management and cost-effectiveness calculation • Develop and evaluate 2 versions during the project • First version due in March 2002 • Incremental evaluation and implementation

  9. M M Y M Y M Y Y Y Y M M What ART-SCENE Seeks….. Scenarios with CREWS-SAVRE • Integrated modelling and computational environment Scenario Analyser Requirement compliance Scenario outcome scenarios i* modelling with REDEPEND scenarios Computed outcomes requirements Patterns and other analyses requirements architecture Computed outcomes Architectures with iUML architectures • A large research challenge that might fail...

  10. The ART-SCENE Architecture Copernicus Goals ReDepend Scenario authoring Archi- tecture Goals, tasks & trade-offs Customer scenarios Scenario elaboration Generated scenarios OCD Scenario analysis Scenario analysis Scenario analysis Scenario analysis iUML New system requirements Scenario walkthrough Patterns Generated scenarios Computed scenario outcomes Computation Engine Requirements structures Measures of system effectiveness

  11. REDEPEND Software Prototype • VISIO2000-based software prototype i* Modelling palette Common i* goal structure Soft goal contribution links

  12. requirements Scenario to acquire Fit criteria Scenario outcome M Y M Test outcome against fit criteria scenarios Y Scenario to analyse Y M architecture Using Patterns in Trade-Off Analysis • Key research question • Analyse platform behaviour with different architectures through scenario analyses to determine requirement compliance How architecture performs in terms of requirements Performance data

  13. Modelling Patterns Using i* Semantics • Link requirements and architectures • Individual agent dependencies • Link system, sub-system and adjacent system agents in terms of goal and soft goal dependencies • Individual means-endlinks • Connect architecture components (tasks at the moment) to system goals/soft goals • Patterns of agent dependencies and means-end links • Set of agent dependencies and means-end links that encapsulate the association of an architecture to a requirement • Define the architecture trade-off space • Negative contribution links between soft goals • Definition of the design trade-off space in terms of soft goals

  14. BAE SYSTEMS Pattern Language • Develop pattern language to model • Knowledge of design trade-offs made about manoeuvring systems of in-service submarines • Stable domain that affords extensive reuse • Elicit from BAE SYSTEMS expert systems engineers • Investigate 3 research questions 1. Can experts agree a set of categories of design trade-off decisions that are reusable in systems engineering? 2. Can experts articulate the essence of the decision categories that distinguish them from other decisions? 3. Can we model this essence using the I* formalism so that the experts agree that it is a accurate representation?

  15. Method for Pattern Modelling • Worked with pairs of BAE SYSTEMS engineers • Developed list of key trade-off decisions • Incremental modelling of patterns • Open-ended 3-hour elicitation sessions with engineers • First-cut modelling of each pattern with i* • Focused 2-hour presentations to change and improve i* pattern model with engineers • Agree and sign-off i* pattern model • Further i* modelling of manoeuvring domain • Standard SD model of manoeuvring agents and their dependencies • Standard SR model of manoeuvring soft goals and their contribution links

  16. Answering Our Research Questions • We posited 3 research questions 1. Can experts agree a set of categories of design trade-off decisions that are reusable in systems engineering? • YES: Results produced a list of >10 basic decision categories 2. Can experts articulate the essence of the decision categories that distinguish them from other decisions? • YES: Experts were able to verbalise and agree discriminating characteristics of each decision category 3. Can we model this essence using the i* formalism so that the experts agree that it is accurate representation? • YES: Four comprehensive i* models of 4 patterns - Experts stated that i* captured most essential characteristics • Apart from elements of the solution architecture • This implies the need for extensions to the semantics….

  17. How To Improve the Patterns • Better representation of solution elements • Architectures of agents, components and connections • Extend with or link to the iUML approach • Continuous as well as discrete solution spaces • Complex systems have continuous solution spaces • Impossible to enumerate all solutions using a qualitative modelling approach • A means of linking characteristics of this solution space with solution architecture and component elements • Attaching performance data to patterns • We can define requirement-architecture trade-offs • Performance data is complex in submarine design • From complex, domain-specific simulation models… But….

  18. ART-SCENE: Putting It All Together • Manoeuvring pattern language • Provides the basis for assessing effectiveness of patterns in trade-off decision-making • Integrate the ART-SCENE prototype and evaluate it using BAE SYSTEMS submarine manoeuvring and Eurocontrol’s air traffic management domain • REDEPEND i* modelling software tool • CREWS-SAVRE scenario elaboration and generation • Architecture modelling using formal iUML approach • Basis for exploring research question • Is it possible to assess conformance of different platform/equipment configurations to whole-system requirements in the context of different operational scenarios

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