1University of TorontoDepartment of Computer Science© Easterbrook 20042Lecture 11:Requirements Modelling A little refresher: What are we modelling? Requirements; Systems; Systems Thinking Role of Modelling in RE Why modelling is important Limitations of modelling Brief overview of modelling languages Modelling principles Abstraction Decomposition Projection ModularityUniversity of TorontoDepartment of Computer Science© Easterbrook 20043Refresher: Definitions Some distinctions: Domain Properties - things in the application domain that are true whether or not weever build the proposed system Requirements - things in the application domain that we wish to be made true bydelivering the proposed system A specification - a description of the behaviours the program must have in order tomeet the requirements Two correctness (verification) criteria: The Program running on a particular Computer satisfies the Specification The Specification, in the context of the given domain properties, satisfies therequirements Two completeness (validation) criteria: We discovered all the important requirements We discovered all the relevant domain propertiesApplication DomainMachine DomainD - domain propertiesR - requirementsC - computersP - programsUniversity of TorontoDepartment of Computer Science© Easterbrook 20044Source: Adapted from Loucopoulos & Karakostas, 1995, p73Subject SystemInformation systemUsesbuildsMaintains informationaboutNeeds informationaboutcontractsUsage SystemDevelopment SystemRefresher: Systems to modelUniversity of TorontoDepartment of Computer Science© Easterbrook 20045Refresher: Systems Thinking2University of TorontoDepartment of Computer Science© Easterbrook 20046Modelling… Modelling can guide elicitation: It can help you figure out what questions to ask It can help to surface hidden requirements i.e. does it help you ask the right questions? Modelling can provide a measure of progress: Completeness of the models -> completeness of the elicitation (?) i.e. if we’ve filled in all the pieces of the models, are we done? Modelling can help to uncover problems Inconsistency in the models can reveal interesting things… e.g. conflicting or infeasible requirements e.g. confusion over terminology, scope, etc e.g. disagreements between stakeholders Modelling can help us check our understanding Reason over the model to understand its consequences Does it have the properties we expect? Animate the model to help us visualize/validate the requirementsUniversity of TorontoDepartment of Computer Science© Easterbrook 20047Source: Adapted from Jackson, 1995, p120-122For every B, atleast one P existssuch that R(P, B)TheapplicationdomainDesignations forthe applicationdomainCommonPropertiesThemodellingdomainDesignationsfor the model’sdomainB = BookP = PersonR = WroteBook: entityPerson: entityauthor: relationRE involves a lot of modelling A model is more than just a description it has its own phenomena, and its own relationships among those phenomena. The model is only useful if the model’s phenomena correspond in a systematic wayto the phenomena of the domain being modelled. Example:Booktitleauthor(0,n)(1,n)nameISBNPersonUniversity of TorontoDepartment of Computer Science© Easterbrook 20048“It’s only a model” There will always be: phenomena in the model that are not present in the application domain phenomena in the application domain that are not in the model A model is never perfect “If the map and the terrain disagree, believe the terrain” Perfecting the model is not always a good use of your time...Source: Adapted from Jackson, 1995, p124-5…every book has atleast one author……every book has aunique ISBN…CommonPhenomena…ghost writers……pseudonyms……anonymity……no two peopleborn on same datewith same name…Booktitleauthor(0,n)(1,n)nameISBNPersonDOBPhenomenanot capturedin the modelPhenomenanot truein the worldUniversity of TorontoDepartment of Computer Science© Easterbrook 20049Source: Adapted from Loucopoulos & Karakostas, 1995, p72-73UML fits in hereChoice of modelling notation natural language extremely expressive and flexible useful for elicitation, and to annotate models for readability poor at capturing key relationships semi-formal notation captures structure and some semantics can perform (some) reasoning, consistency checking, animation, etc. E.g. diagrams, tables, structured English, etc. mostly visual - for rapid communication with a variety of stakeholders formal notation precise semantics, extensive reasoning possible Underlying mathematical model (e.g. set theory, FSMs, etc) very detailed models (may be more detailed than we need) RE formalisms are for conceptual modelling, hence differ from most computerscience formalisms3University of TorontoDepartment of Computer Science© Easterbrook 200410Desiderata for Modelling Notations Implementation Independence does not model data representation,internal organization, etc. Abstraction extracts essential aspectse.g. things not subject to frequentchange Formality unambiguous syntax rich semantic theory Constructability can construct pieces of the model tohandle complexity and size construction should facilitatecommunication Ease of analysis ability to analyze for ambiguity,incompleteness, inconsistency Traceability ability to cross-reference elements ability to link to design,implementation, etc. Executability can animate the model, to compare itto reality Minimality No redundancy of concepts in themodelling schemei.e. no extraneous choices of how torepresent somethingSource: Adapted from Loucopoulos & Karakostas, 1995, p77University of TorontoDepartment of Computer Science© Easterbrook 200411Survey of Modelling Techniques Modelling Enterprises Goals & objectives Organizational structure Tasks & dependencies Agents, roles, intentionality Modelling Information & Behaviour Information Structure Behavioral views Scenarios and Use Cases State machine models Information flow Timing/Sequencing requirements Modelling System Qualities (NFRs) All the ‘ilities’: Usability, reliability, evolvability, safety,security, performance, interoperability,…Organization modelling:i*, SSM, ISACGoal modelling:KAOS,
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