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7/21/2019 06 Goal Modeling
1/51
RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
Goal Modeling
1
7/21/2019 06 Goal Modeling
2/51
RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
We Will Cover
What is a goal? Where do goals come from?
What is a goal model?
When to use goal models? How do goal models relate to UML models?
Why use goal models?
Capturing the goal model
2
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
What is a goal?
A stakeholder objective for the system The system includes the software and its
environment
Who are stakeholders?
Anyone who has an interest in the system Customers, end users, system developers,
system maintainers...
What is a goal model?
A hierarchy of goals
Relates the high-level goals to low-level systemrequirements
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
Goal or Not a Goal?
Goal Examples: Credit card information is kept private
Credit card information is accurate
Safe transportation
Highly reliability
Non-goal Examples: The system will be implemented in C++
The paint colors for the cars will be yellow,orange, and red
4
7/21/2019 06 Goal Modeling
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
Goal Exercise
Order the list of goals from high-levelconcern to low-level concern
User receives a request for a timetable from asystem
Collect timetables by system
Schedule meeting
System collect timetables from user Collect timetables
7/21/2019 06 Goal Modeling
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
Goal Exercise
Order the list of goals from high-levelconcern to low-level concern
Schedule meeting
Collect timetables
Collect timetables by system
System collect timetables from user
User receives a request for a timetable
7/21/2019 06 Goal Modeling
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
Types of Goals
Functional (Hard) Describe functions the system will perform
Well defined criteria for satisfaction
E.g., System collects timetables from user
Non-functional (Soft or fuzzy) Describe desired system qualities
Hard to define; satisficed rather than satisfied
Reliability E.g., System should be reliable
Quality E.g., System should be high quality.
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
Where do goals come from?
Conveyed by stakeholders Disclosed in requirements documents
Analysis of similar or current system
Elaborating other goals
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2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
Goal Exercise
Identify goals in the following paragraph An Adaptive Regional Forecasting Ecological Observatory (ARFEO) is proposed asmeans to implement an operational, run-time configurable, and adaptable ecologicalobservatory to be used for regional ecological forecasting. ARFEO has three maindimensions. First, using a holistic perspective of environment, we will use sensorsthat are analogous to human and organism senses to monitor the environment and itschanges. As such, ARFEO can be configured to observe and answer ecologicalquestions specific to one class of sensory input or across multiple sensory inputs.
Second, ARFEO will use a cyberinfrastructure comprising smart, heterogeneoussensor networks, small-scale and GRID-scale distributed computing [6]. ARFEOsarchitecture will be a distributed design which will enable users to perform small andcomplex computations and transparent integration of data and analysis. ARFEO willenable a user to adapt monitoring capabilities at run-time, thus allowing a user tocustomize the configuration to specific needs and questions. Finally, ARFEO willemphasize the reuse and synergistic integration of existing analysis and visualizationtechniques to include in the computational toolkit for processing the sensor andancillary data, as well as metadata. A key part of ARFEO will be the development of
processes to make use of the toolkit element to support the analysis and visualizationcapabilities.
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
When to use goal models
Early requirements engineering Focus on identifying problems
Exploring system solutions and alternatives
Done before UML modeling
Design Code TestLate REEarly RE
Why? What? How?
7/21/2019 06 Goal Modeling
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
Why use goal models?
Give rationale for requirements Identify stable information
Guide requirement elaboration
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
The Goal Model
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
Running Example
Meeting Scheduler Assists the initiator in scheduling a meeting
Meeting should be convenient for participants
Participants should be available
Modeled using the i* goal notation
13Example adapted from the RE06 keynote given by John Mylopoulos
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
Goal Model
Chooseschedule
Schedule
meeting
Collecttimetables
Bysystem
Byperson
Collect fromagents
Collect fromusers
Receive
requestSend
request
Manually Automatically
Goals are refined into
subgoals that elaboratehow the goal is
achieved
Example adapted from the RE06 keynote given by John Mylopoulos
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
AND Refinement
15
Chooseschedule
Schedule
meeting
Collecttimetables
Bysystem
Byperson
Collect fromagents
Collect fromusers
Receive
requestSend
request
Manually Automatically
AND
AND AND
AND
All of the
subgoals must beachieved for the
goal to beachieved
Example adapted from the RE06 keynote given by John Mylopoulos
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
OR Refinement
16
Chooseschedule
Schedule
meeting
Collecttimetables
Bysystem
Byperson
Collect fromagents
Collect fromusers
Receive
requestSend
request
Manually Automatically
AND
ANDAND
OR OR
OR OR
OR OR
AND
At least one of the
subgoals must beachieved for the
goal to be achieved
Example adapted from the RE06 keynote given by John Mylopoulos
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
Interpretations of OR Refinements
17
Chooseschedule
Schedule
meeting
Collecttimetables
Bysystem
Byperson
Collect fromagents
Collect fromusers
Receive
requestSend
request
Manually Automatically
AND
ANDAND
OR OR
OR OR
OR OR
AND
Static systems-alternative solutions
Adaptive systemspoints of variation
Example adapted from the RE06 keynote given by John Mylopoulos
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
Softgoals
Minimal
conflicts
Good Qualityschedule
Goodparticipation
Collection
effort
Matchingeffort
Minimaleffort
Minimaldisturbances
Accurateconstraints
AND
AND
AND
AND
Example adapted from the RE06 keynote given by John Mylopoulos
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
Modeling Softgoals
Used to evaluate alternatives Helps (+)
Makes (++)
Hurts (-) Breaks (--)
Example adapted from the RE06 keynote given by John Mylopoulos
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
Contributions to Softgoals
Chooseschedule
Schedulemeeting
Minimal
conflicts
Good Qualityschedule
Goodparticipation
Manually Automatically
AND
OR
OR
!
+
+
!
AND
AND
Example adapted from the RE06 keynote given by John Mylopoulos
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
Construction Process
21
Minimal
conflicts
Good Qualityschedule
Goodparticipation
Minimaldisturbances
Accurateconstraints
Collectioneffort
Matchingeffort
Minimal
effort
AND
AND AND
AND
1. DefineSoftgoals
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
2. DefineHardgoals
Steps 1 & 2may be
iterative
Schedulemeeting
Collecttimetables
Bysystem
Byperson
Collect fromagents
Collect fromusers
Receive
requestSend
request
Minimal
conflicts
Good Qualityschedule
Goodparticipation
Minimaldisturbances
Accurateconstraints
Collectioneffort
Matchingeffort
Minimal
effort
Chooseschedule
Manually Automatically
AND
ANDAND
OR OROR
OR OR
OR
AND
AND
AND
AND
AND
22Example adapted from the RE06 keynote given by John Mylopoulos
Construction Process
7/21/2019 06 Goal Modeling
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.23
Schedulemeeting
Collecttimetables
Bysystem
Byperson
Collect fromagents
Collect fromusers
Receive
requestSend
request
Minimal
conflicts
Good Qualityschedule
Goodparticipation
Minimaldisturbances
Accurateconstraints
Collectioneffort
Matchingeffort
Minimal
effort
Chooseschedule
Manually Automatically
AND
ANDAND
OR OROR
OR OR
OR
+
!
!
+
!
+
+
!
!
+
!
+
AND
AND
AND
AND
AND
Example adapted from the RE06 keynote given by John Mylopoulos
Construction Process
3. Evaluatehardgoalcontributionto softgoals
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
Integrating Goals with Other
Models
24
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
Integrated Use of Goals
KAOS Refining goals into requirements
4 models Goal
Agent Operationalization
Object
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
KAOS Goal Model
Agent - active system component
Object - inactive system component
Operation - an action an agent takes to achieve a goal
Requirement - a goal for which an automated component is responsible
Expectation - a goal for which a human is responsible Safe Transportation
Maintain Knowledge of Other Train Locations
Manually Radio Other Train Conductors
Radio Automatically Sense Other Train Locations
TrainRadio Other Train
SoftwareSensor
Human
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
KAOS Goal Model
Agent - active system component
Object - inactive system component
Operation - an action an agent takes to achieve a goal
Requirement - a goal for which an automated component is responsible
Expectation - a goal for which a human is responsible Safe Transportation
Maintain Knowledge of Other Train Locations
Manually Radio Other Train Conductors
Radio Automatically Sense Other Train Locations
TrainRadio Other Train
SoftwareSensor
Human
7/21/2019 06 Goal Modeling
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
KAOS Goal Model
Agent - active system component
Object - inactive system component
Operation - an action an agent takes to achieve a goal
Requirement - a goal for which an automated component is responsible
Expectation - a goal for which a human is responsible Safe Transportation
Maintain Knowledge of Other Train Locations
Manually Radio Other Train Conductors
Radio Automatically Sense Other Train Locations
TrainRadio Other Train
SoftwareSensor
Human
7/21/2019 06 Goal Modeling
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
KAOS Goal Model
Agent - active system component
Object - inactive system component
Operation - an action an agent takes to achieve a goal
Requirement - a goal for which an automated component is responsible
Expectation - a goal for which a human is responsible Safe Transportation
Maintain Knowledge of Other Train Locations
Manually Radio Other Train Conductors
Radio Automatically Sense Other Train Locations
TrainRadio Other Train
SoftwareSensor
Human
7/21/2019 06 Goal Modeling
30/51
RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
KAOS Goal Model
Agent - active system component
Object - inactive system component
Operation - an action an agent takes to achieve a goal
Requirement - a goal for which an automated component is responsible
Expectation - a goal for which a human is responsible Safe Transportation
Maintain Knowledge of Other Train Locations
Manually Radio Other Train Conductors
Radio Automatically Sense Other Train Locations
TrainRadio Other Train
SoftwareSensor
Human
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
KAOS Goal Model
Agent - active system component
Object - inactive system component
Operation - an action an agent takes to achieve a goal
Requirement - a goal for which an automated component is responsible
Expectation - a goal for which a human is responsible Safe Transportation
Maintain Knowledge of Other Train Locations
Manually Radio Other Train Conductors
Radio Automatically Sense Other Train Locations
TrainRadio Other Train
SoftwareSensor
Human
7/21/2019 06 Goal Modeling
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
KAOS Goal Model
Agent - active system component
Object - inactive system component
Operation - an action an agent takes to achieve a goal
Requirement - a goal for which an automated component is responsible
Expectation - a goal for which a human is responsible Safe Transportation
Maintain Knowledge of Other Train Locations
Manually Radio Other Train Conductors
Radio Automatically Sense Other Train Locations
TrainRadio Other Train
SoftwareSensor
Human
7/21/2019 06 Goal Modeling
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
KAOS Goal Model
Agent - active system component
Object - inactive system component
Operation - an action an agent takes to achieve a goal
Requirement - a goal for which an automated component is responsible
Expectation - a goal for which a human is responsible Safe Transportation
Maintain Knowledge of Other Train Locations
Manually Radio Other Train Conductors
Radio Automatically Sense Other Train Locations
TrainRadio Other Train
SoftwareSensor
Human
7/21/2019 06 Goal Modeling
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
KAOS Goal Model
Agent - active system component
Object - inactive system component
Operation - an action an agent takes to achieve a goal
Requirement - a goal for which an automated component is responsible
Expectation - a goal for which a human is responsible Safe Transportation
Maintain Knowledge of Other Train Locations
Manually Radio Other Train Conductors
Radio Automatically Sense Other Train Locations
TrainRadio Other Train
SoftwareSensor
Human
7/21/2019 06 Goal Modeling
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
KAOS Goal Model
Agent - active system component
Object - inactive system component
Operation - an action an agent takes to achieve a goal
Requirement - a goal for which an automated component is responsible
Expectation - a goal for which a human is responsible Safe Transportation
Maintain Knowledge of Other Train Locations
Manually Radio Other Train Conductors
Radio Automatically Sense Other Train Locations
TrainRadio Other Train
SoftwareSensor
Human
7/21/2019 06 Goal Modeling
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
KAOS Goal Model
Agent - active system component
Object - inactive system component
Operation - an action an agent takes to achieve a goal
Requirement - a goal for which an automated component is responsible
Expectation - a goal for which a human is responsible Safe Transportation
Maintain Knowledge of Other Train Locations
Manually Radio Other Train Conductors
Radio Automatically Sense Other Train Locations
TrainRadio Other Train
SoftwareSensor
Human
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
KAOS Agent Model
Objective: depicts agent responsibilities Agent models can be inferred from goal models
27
Sensor
Maintain Knowledge of Other Train Locations
Manually Radio Other Train Conductors
Automatically Sense Other Train Locations
Manually Radio Other Train Conductors
SoftwareSensor
Human
Automatically Sense Other Train Locations
Software
Human
Goal Model Agent Model
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
KAOS Agent Model
Objective: depicts agent responsibilities Agent models can be inferred from goal models
27
Sensor
Maintain Knowledge of Other Train Locations
Manually Radio Other Train Conductors
Automatically Sense Other Train Locations
Manually Radio Other Train Conductors
SoftwareSensor
Human
Automatically Sense Other Train Locations
Software
Human
Human
Manually Radio Other Train Conductors
Goal Model Agent Model
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
KAOS Agent Model
Objective: depicts agent responsibilities Agent models can be inferred from goal models
27
Sensor
Maintain Knowledge of Other Train Locations
Manually Radio Other Train Conductors
Automatically Sense Other Train Locations
Manually Radio Other Train Conductors
SoftwareSensor
Human
Automatically Sense Other Train Locations
Software
Human
Goal Model Agent Model
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
KAOS Agent Model
Objective: depicts agent responsibilities Agent models can be inferred from goal models
27
Sensor
Maintain Knowledge of Other Train Locations
Manually Radio Other Train Conductors
Automatically Sense Other Train Locations
Manually Radio Other Train Conductors
SoftwareSensor
Human
Automatically Sense Other Train Locations
Software
Human
Manually Radio Other Train Conductors
Alert Passengers to Delays
Human
Manually Slow the Train
Goal Model Agent Model
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
KAOS Agent Model
Objective: depicts agent responsibilities Agent models can be inferred from goal models
27
Sensor
Maintain Knowledge of Other Train Locations
Manually Radio Other Train Conductors
Automatically Sense Other Train Locations
Manually Radio Other Train Conductors
SoftwareSensor
Human
Automatically Sense Other Train Locations
Software
Human
Goal Model Agent Model
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
KAOS Operationalization Model Objective: specifies the operations that agents must
perform to achieve the goals
28
Door
Safe Transportation
Train Doors Closed While Moving
Train Begins to Move
Close Door
Close Door Request
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
KAOS Operationalization Model Objective: specifies the operations that agents must
perform to achieve the goals
28
Door
Safe Transportation
Train Doors Closed While Moving
Train Begins to Move
Close Door
Close Door Request
Door
Train Doors Closed While Moving
Train Begins to Move
Close Door
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
KAOS Object Model
Objective: further specifies objects used in the goal model The syntax is similar to a that of a UML class diagram
29
TrackSegment
SpeedLimit
Gate
Loc
Status
Switch
position
Speed
Loc
Train
Track
hasGateOn
On Track
Following
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
Integrated Use of Goals
KAOS Refining goals into requirements
4 models Goal
Agent Operationalization
Object
i* Relates goals to the organization context
2 models Actor (Strategic) Dependency Model
Actor (Strategic) Rationale Model
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2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
i* Actor Dependency Model
Dependencies between actors - An actor is a black box
31Diagram from Towards Modelling and Reasoning Support for Early-Phase Requirements Engineeringby Eric Yu
D
MeetingInitiator
ProposedDate(m)
Agreement(m,p)
AttendsMeeting(ip,m)
Assured(AttendsMeeting(ip,m))
AttendsMeeting(p,m)
MeetingBeScheduled(m)
MeetingScheduler
Important
Participant
X
ISA
DD
DD
D
DD
D
D D
DD
D
D
D
EnterDateRange (m)
Enter
AvailDates (m) Meeting
Participant
D
D
D
D
D
D
D
D
Depender Dependee
LEGEND
O XOpen (uncommitted) Critical
Resource Dependency
Task Dependency
Goal Dependency
Softgoal Dependency
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
i* Actor Dependency Model
Dependencies between actors - An actor is a black box
31Diagram from Towards Modelling and Reasoning Support for Early-Phase Requirements Engineeringby Eric Yu
D
MeetingInitiator
ProposedDate(m)
Agreement(m,p)
AttendsMeeting(ip,m)
Assured(AttendsMeeting(ip,m))
AttendsMeeting(p,m)
MeetingBeScheduled(m)
MeetingScheduler
Important
Participant
X
ISA
DD
DD
D
DD
D
D D
DD
D
D
D
EnterDateRange (m)
Enter
AvailDates (m) Meeting
Participant
D
D
D
D
D
D
D
D
Depender Dependee
LEGEND
O XOpen (uncommitted) Critical
Resource Dependency
Task Dependency
Goal Dependency
Softgoal Dependency
D
D
D
D
D
D
D
D
Depender Dependee
LEGEND
O XOpen (uncommitted) Critical
Resource Dependency
Task Dependency
Goal Dependency
Softgoal Dependency
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
i* Actor Dependency Model
Dependencies between actors - An actor is a black box
31Diagram from Towards Modelling and Reasoning Support for Early-Phase Requirements Engineeringby Eric Yu
D
MeetingInitiator
ProposedDate(m)
Agreement(m,p)
AttendsMeeting(ip,m)
Assured(AttendsMeeting(ip,m))
AttendsMeeting(p,m)
MeetingBeScheduled(m)
MeetingScheduler
ImportantParticipant
X
ISA
DD
DD
D
DD
D
D D
DD
D
D
D
EnterDateRange (m)
Enter
AvailDates (m) Meeting
Participant
D
D
D
D
D
D
D
D
Depender Dependee
LEGEND
O XOpen (uncommitted) Critical
Resource Dependency
Task Dependency
Goal Dependency
Softgoal Dependency
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2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
i* Actor Rationale Model
32
Internal actor relationships - An actor is a white box
Agreeable(Meeting,Date)
AgreeToDate
Convenient(Meeting,Date)
ProposedDate
Agreement
ObtainAgreement
ObtainAvailDates
FindAgreeable Slot
MergeAvailDates
Quick MeetingBeScheduled
ScheduleMeeting
AttendMeeting
ParticipateInMeeting
LetScedulerScheduleMeeting
ScheduleMeeting
MeetingInitiator
MeetingSchedulerMeetingBe
Scheduled
FindAgreeableDateUsingScheduler
FindAgreeableDateByTalking
ToInitiator
Quality(ProposedDate)
UserFriendly
ArrangeMeeting
RicherMedium
Enter
AvailDates
OrganizeMeeting
MeetingParticipant
AttendsMeeting
EnterDateRange
D
D
D
D
DD
D D
DD
D
D
+ !
!
+
+
!
+
+! +
+
LEGEND
Goal
Task
Resource
Soft!Goal
Task!Decompo! sition linkMeans!ends link
Contribution to softgoalsActor
Actor Boundary
+!
LowEffort
LowEffort
Diagram from Towards Modelling and Reasoning Support for Early-Phase Requirements Engineeringby Eric Yu
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RE
2006-07 Betty H.C. Cheng. This presentation is available free for non-commercial use with attributionunder a creative commons license.
i* Actor Rationale Model
32
Internal actor relationships - An actor is a white box
Agreeable(Meeting,Date)
AgreeToDate
Convenient(Meeting,Date)
ProposedDate
Agreement
ObtainAgreement
ObtainAvailDates
FindAgreeable Slot
MergeAvailDates
Quick MeetingBeScheduled
ScheduleMeeting
AttendMeeting
ParticipateInMeeting
LetScedulerScheduleMeeting
ScheduleMeeting
MeetingInitiator
MeetingSchedulerMeetingBe
Scheduled
FindAgreeableDateUsingScheduler
FindAgreeableDateByTalking
ToInitiator
Quality(ProposedDate)
UserFriendly
ArrangeMeeting
RicherMedium
Enter
AvailDates
OrganizeMeeting
MeetingParticipant
AttendsMeeting
EnterDateRange
D
D
D
D
DD
D D
DD
D
D
+ !
!
+
+
!
+
+! +
+
LEGEND
Goal
Task
Resource
Soft!Goal
Task!Decompo! sition linkMeans!ends link
Contribution to softgoalsActor
Actor Boundary
+!
LowEffort
LowEffort
Diagram from Towards Modelling and Reasoning Support for Early-Phase Requirements Engineeringby Eric Yu
LEGEND
Goal
Task
Resource
Soft!Goal
Task!Decompo! sition linkMeans!ends link
Contribution to
softgoalsActor
Actor Boundary
+!
7/21/2019 06 Goal Modeling
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RE i* Actor Rationale Model Internal actor relationships - An actor is a white box
Agreeable(Meeting,Date)
AgreeToDate
Convenient(Meeting,Date)
ProposedDate
Agreement
ObtainAgreement
ObtainAvailDates
FindAgreeable Slot
MergeAvailDates
Quick MeetingBeScheduled
ScheduleMeeting
AttendMeeting
ParticipateInMeeting
LetScedulerScheduleMeeting
ScheduleMeeting
MeetingInitiator
MeetingSchedulerMeetingBe
Scheduled
FindAgreeableDateUsingScheduler
FindAgreeableDateByTalking
ToInitiator
Quality(ProposedDate)
UserFriendly
ArrangeMeeting
RicherMedium
Enter
AvailDates
OrganizeMeeting
MeetingParticipant
AttendsMeeting
EnterDateRange
D
D
D
D
DD
D D
DD
D
D
+ !
!
+
+
!
+
+! +
+
LEGEND
Goal
Task
Resource
Soft!Goal
Task!Decompo! sition linkMeans!ends link
Contribution to softgoalsActor
Actor Boundary
+!
LowEffort
LowEffort