Analyzing Goal Models –
Different Approaches and How
to Choose Among Them
Jennifer Horkoff1
Eric Yu2
1Department
of Computer Science
2Faculty of Information
University of Toronto, Canada
SAC’11 RE Track
Goal-Oriented Requirements Engineering (GORE)

GORE has received much attention in RE research as a
means of:

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
Generally, GORE frameworks allow for:

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Understanding the motivations for system requirements
Helping to ensure that the right system is built
Representation of stakeholder goals
Goals may be assigned to an agent (stakeholder or system)
Goals may have relationships to other goals, often describing
achievement
Several goal modeling frameworks

KAOS, GBRAM, AGORA, NFR, i*, Tropos, GRL, …
Analyzing Goal Models, Horkoff & Yu
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Goal-Oriented Requirements Engineering (GORE)
Example:
Counseling
Organization i*
Model (Horkoff &
Yu, 2009)
We can analyze the contents of goal
models systematically…
Analyzing Goal Models, Horkoff & Yu
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Example: Qualitative, Interactive Forward Satisfaction
Analysis of Goal-Oriented Models
What is the effect of
using a Cybercafe/
Portal/ Chat Room?”
Horkoff, Yu: Evaluating Goal Achievement in
Enterprise Modeling
Analyzing Goal Models, Horkoff & Yu
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Models and Analysis become Complex
Analyzing Goal Models, Horkoff & Yu
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Goal Model Analysis

Many different analysis techniques for goal models have
been introduced:

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
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Abundance of approaches is encouraging from a research
perspective, but…
From a user or practitioner perspective can be confusing



Propagate satisfaction values through the model
Measure metrics over the model
Apply planning techniques
Run simulations
Perform checks over models
What are the differences?
When would I use one and not another?
Limits adoption
Analyzing Goal Models, Horkoff & Yu
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Motivating Questions

Survey of methods
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Analysis benefits


What methods are available?
What types of analysis questions can these methods answer?
What types of goal modeling constructs do the procedures
support?
What information is needed in order to use the methods?
What are some of the potential benefits of goal model analysis in
the requirements process?
Mapping and Selection


What available methods can be applied to achieve which kinds of
usage objectives?
How can we use this information to advise on selection?
Analyzing Goal Models, Horkoff & Yu
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Survey of Goal Model Analysis Procedures
Approach
Paper
Additional Notation
Supported
Satisf Satisf Human Metrics Plan- Simu- Model Qual Quant Binary Depend Soft- Contribut
Forwds Backwds Interv
ning lation Check
-encies goals -ion Links
Y
Giorgini et al. [21]
Y
Giorgini et al.[22]
Y
Giorgini et al. [23]
Y
Horkoff & Yu [26]
Y
Maiden et al. [33]
Y
Amyot et al. [1]
Y
Asnar & Giorgini [3]
Y
Letier & vLams. [31]
Y
Horkoff & Yu [27]
Y
Wang et al. [35]
Y
Bryl et al. [6]
N
Bryl et al. [7]
N
Asnar et al. [4]
Y
Gans et al [18]
N
Wang & Lesper. [34] N
Gans et al. [16] [18]
N
Gans et al. [17]
N
Fuxman et al. [14] [15] N
Giorgini et al.[20]
N
Bryl et al.[8]
N
Chung et al. [9]
N
N
Y
Y
N
N
N
Y
Y
Y
Y
N
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Y
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Procedures
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Analysis Results
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Summary
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Dimensions
Y
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Analyzing Goal Models, Horkoff & Yu
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A Survey of GORE Analysis Techniques

Summarize results over the following points:

Algorithm Approach


Format of analysis results


Satisfaction Forwards, Backwards, Human Intervention, Metrics,
Planning, Simulation, and Model Checking
Qualitative (
), quantitative (0.37), binary (T/F)
Goal-oriented concepts supported (beyond AND/OR)

Dependencies, softgoals, contribution links
Approach
Paper
Additional Notation
Supported
Satisf
Satisf Human Metrics Plan- Simu- Model Qual Quant Binary Depend Soft- Contribut
Forwds Backwds Interv
ning lation Check
-encies goals -ion Links
Smith et al. [1] Y

N
Y
N
Analysis Results
N
N
N
Y
N
Y
N
Y
Y
Additional information required beyond typical goal model
constructs

e.g., priority, probabilities, events, delegations, and trust.
Analyzing Goal Models, Horkoff & Yu
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Satisfaction Analysis

Example Analysis Questions:


What is the effect of this alternative?
Can this goal be satisfied?

Evaluates the satisfaction or denial of goals given a
functional or design alternative

Values are propagated forward or backward throughout
the model

Qualitative or quantitative approaches

Techniques take different approaches to resolving
multiple values for incoming goals:

Adding evidence, combine using probabilistic rules, separate
evidence, fixed rules, human judgment
Analyzing Goal Models, Horkoff & Yu
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Satisfaction Analysis
Approach
Paper
Chung et al.
[9]
Giorgini et al.
[21]
Giorgini et
al.[22]
Giorgini et al.
[23]
Horkoff & Yu
[26]
Maiden et al.
[33]
Amyot et al.
[1]
Asnar &
Giorgini [3]
Letier &
vLams. [31]
Horkoff & Yu
[27]
Wang et al.
[35]
Analysis Results
Additional Notation
Supported
Satisf
Satisf Human Metrics Plan- Simu- Model Qual Quant Binary Depend Soft- Contribut
Forwds Backwds Interv
ning lation Check
-encies goals -ion Links
Y
N
Y
N
N
N
N
Y
N
Y
N
Y
Y
Y
N
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N
Y
Y
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Analyzing Goal Models, Horkoff & Yu
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Metrics


Example Analysis Questions:

How secure is the system represented by the model?

How risky is a particular alternative for a stakeholder?
Structural properties of the model and construct classifications
are used to calculate metrics


Metrics often represent non-functional requirements


Examples: predictability, security, privacy, accuracy, etc.
They can also represent model properties:


Example: counts of dependency classifications (instance, model,
duplicate, hidden) in a Strategic Dependency (SD)
Examples: completeness, consistency and correctness
Metrics can be local or global
Analyzing Goal Models, Horkoff & Yu
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Metrics
Approach
Paper
Franch &
Maiden [12]
Franch et al.
[13]
Franch [11]
Kaiya et al.
[30]
Analysis Results
Additional Notation
Supported
Satisf
Satisf Human Metrics Plan- Simu- Model Qual Quant Binary Depend Soft- Contribut
Forwds Backwds Interv
ning lation Check
-encies goals -ion Links
N
N
N
Y
N
N
N
N
Y
N
Y
Y
N
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N
Y
N
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M
Y
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Y
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Y
Y
Y
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Y
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M
Analyzing Goal Models, Horkoff & Yu
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Planning

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Example Analysis Questions:

What actions must be taken to satisfy goals?

What are the best plans according to certain criteria?
Work has applied AI-type planning to find satisfactory
sequences of actions in models
Requires definition of axioms that express possible goal
decompositions and delegations



Expresses the capabilities of actors in a model
A planner finds a delegation of goals to actors which
fulfills model goals
Plans are evaluated by some criteria
Analyzing Goal Models, Horkoff & Yu
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Planning
Approach
Paper
Additional Notation
Supported
Satisf
Satisf Human Metrics Plan- Simu- Model Qual Quant Binary Depend Soft- Contribut
Forwds Backwds Interv
ning lation Check
-encies goals -ion Links
Bryl et al. [6] N
Bryl et al. [7] N
Asnar et al. [4] Y
N
N
Y
N
Y
Y
Y
Y
Y
Analysis Results
Y
Y
Y
N
N
N
N
Y
N
N
M
Y
Analyzing Goal Models, Horkoff & Yu
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Y
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Y
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Simulation

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Example Analysis Questions:

What happens when an alternative is selected?

Are there unexpected properties in a simulation?
Adds temporal information including pre- and postconditions to models


Translated to ConGolog (situation calculus) programs for
simulation
Extensions simulate confidence, trust and distrust
Analyzing Goal Models, Horkoff & Yu
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Simulation
Approach
Paper
Gans et al [18]
Wang &
Lesper. [34]
Gans et al.
[16] [18]
Gans et al.
[17]
Analysis Results
Additional Notation
Supported
Satisf
Satisf Human Metrics Plan- Simu- Model Qual Quant Binary Depend Soft- Contribut
Forwds Backwds Interv
ning lation Check
-encies goals -ion Links
N
N
Y
N
N
Y
Y
N
N
Y
Y
N
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Y
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Analyzing Goal Models, Horkoff & Yu
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Model Checking

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Example Analysis Questions:

Is it possible to achieve a particular goal?

Is the model consistent?
Models are expanded/converted to a temporal formalism



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Includes expressions of creation, fulfillment and invariant
properties
First order temporal logic statements are used to
represent desired constraints
Model checker is used to validate properties and check for
consistency
Further work adds in checks for security and trust
Analyzing Goal Models, Horkoff & Yu
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Model Checking
Approach
Paper
Additional Notation
Supported
Satisf
Satisf Human Metrics Plan- Simu- Model Qual Quant Binary Depend Soft- Contribut
Forwds Backwds Interv
ning lation Check
-encies goals -ion Links
Fuxman et al.
N
[14] [15]
Giorgini et
N
al.[20]
Bryl et al.[8] N
Analysis Results
N
M
N
N
N
Y
N
N
Y
Y
Y
N
N
N
N
N
N
Y
N
N
Y
Y
N
N
N
N
N
Y
N
Y
N
N
Y
Y
N
N
Analyzing Goal Models, Horkoff & Yu
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Tabular Summary
Approach
Paper
Additional Notation
Supported
Satisf Satisf Human Metrics Plan- Simu- Model Qual Quant Binary Depend Soft- Contribut
Forwds Backwds Interv
ning lation Check
-encies goals -ion Links
Y
Giorgini et al. [21]
Y
Giorgini et al.[22]
Y
Giorgini et al. [23]
Y
Horkoff & Yu [26]
Y
Maiden et al. [33]
Y
Amyot et al. [1]
Y
Asnar & Giorgini [3]
Y
Letier & vLams. [31]
Y
Horkoff & Yu [27]
Y
Wang et al. [35]
Y
Bryl et al. [6]
N
Bryl et al. [7]
N
Asnar et al. [4]
Y
Gans et al [18]
N
Wang & Lesper. [34] N
Gans et al. [16] [18]
N
Gans et al. [17]
N
Fuxman et al. [14] [15] N
Giorgini et al.[20]
N
Bryl et al.[8]
N
Chung et al. [9]
N
N
Y
Y
N
N
N
Y
Y
Y
Y
N
N
Y
N
N
N
N
N
N
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Analysis Results
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20
Information Required by each Procedure
1
2
3
Additional Information
Goal Cost
Risk
Textual Arguments
4
5
6
Probabilistic Information
Events and Treatments
Importance/Priority
7
8
Actor Capabilities
(Pre/Post) Conditions/ Temporal
Information
9
Delegation/Ownership
10
Trust
11
12
13
14
Speech Acts
Confidence and Distrust
Preferences
Cardinalities
Required by
Satisfaction Analysis: [23][4][22][3], Planning: [6]
Satisfaction Analysis: [3], Planning: [4]
Satisfaction Analysis:[33],
Metrics, Model Checking: [30]
Satisfaction Analysis: [23] [31]
Satisfaction Analysis: [3]
Satisfaction Analysis: [1],
Metrics: [13] [1], Simulation: [34]
Planning: [6] [7] [4], Model Checking: [8]
Simulation: [34] [18] [18] [16] [17],
Model Checking: [15] [14]
Model Checking: [19] [8]
Planning: [4], Simulation: [17],
Model Checking: [20][8]
Simulation: [17]
Simulation: [17]
Model Checking: [30]
Simulation:[34], Model Checking: [14]
Analyzing Goal Models, Horkoff & Yu
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Goal Model Analysis Objectives

Using capabilities of techniques in our survey, as well as
our own experience in modeling and analysis, we list
categories of objectives for goal model analysis


List is likely not complete
Objective Categories (goal model analysis can help…):







Understand the domain
Communicate
Improve the model
Make scoping decisions
Prompt requirements elicitation
Improve requirements
Design a system
Analyzing Goal Models, Horkoff & Yu
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Mapping Procedures to Objectives

We have made suggestions concerning what procedures may map to
what objectives



Each mapping can be considered as a hypothesis
We have included guiding questions with each objective to help
motivate the mapping and guide users
Example mappings:
Category
Guidelines
Recommended Procedures
Domain
QU1. Does the domain contain a high Yes. Try: Agent Approaches: i*/GRL Satisfaction Analysis
Understanding degree of social interaction, have many ([1][26][27][33]) i* Metrics ([11][12][13]) Tropos Metrics, Planning,
stakeholders with differing goals, or or Model Checking ([4][6][7][8][14][15][19]) SNET([16][17][18])
involve many interacting systems?
Requirements QR1. Are you working with a system Yes. Try: Analysis over Specific Constructs or Metric
Improvement where safety/security/ privacy/risks or Approaches: KAOS([31]) i* Metrics([11][12][13]) AGORA([30])
other specific properties are critical Tropos Risk, Trust, and Security([3][4] [8][19]) SNET Trust([17])
considerations?

Downloadable interactive mapping table:
www.cs.utoronto.ca/~jenhork/GOREAnalysisSelectionTable.zip
Analyzing Goal Models, Horkoff & Yu
23
Mapping of Procedures to Objectives
Category
Guidelines
Recommended Procedures
Domain
QU1. Does the domain contain a high degree of social interaction, have many stakeholders with Yes. Try: Agent Approaches: i*/GRL Satisfaction Analysis ([1][26][27][33]) i* Metrics
Understanding differing goals, or involve many interacting systems?
([11][12][13]) Tropos Metrics, Planning, or Model Checking ([4][6][7][8][14][15][19])
SNET([16][17][18])
QU2. Do you need to understand details of the system at this point? Do you have access to Yes. Try: Quantitative or Detailed Information: Tropos Probabilistic Satisfaction Analysis
detailed information such as cost, probabilities, and conditions? Can you express necessary or ([3][21][22][23]) KAOS Satisfaction Analysis ([31]) GRL Quant. Analysis ([1]) i* Quant. Metrics
desired domain properties?
([11][12][13]) Tropos Planning ([4][6][7][8]) Tropos Modeling Checking ([8][14][15][19])
SNET([16][17][18][18]) i* Simulation([34]), or Model Checking: Tropos ([8][14][15][19])
SNET([16][18])
Communication QC1. Do you need to communicate with stakeholders? Validate requirements in the model? Yes. Try: Forward Satisfaction Approaches: NFR([9]) Tropos([3][21][22][23]) KAOS([31])
Justify recommendations?
i*([26][33]) GRL([1])
Model
QM1. Are you confident in the accuracy, structure, and completeness of domain knowledge and No. Try: Interactive Approaches: NFR([9]) i*([26][27][33]) Tropos([4][7]) SNET([16][18]) i*
Improvement models?
Metrics([11])
QM2. Would you like to verify critical properties over the model?
Yes. Try: Model Checking: Tropos([8][14][15][19]) SNET([16][18])
Scoping
QS1. Do you need to determine system scope?
Yes. Try: Agent Approaches: i*/GRL Satisfaction Analysis ([1][26] [27][33]) i* Metrics
([11][12][13]) Tropos Metrics, Planning, or Model Checking ([4][6][7][8][14][15][19]) SNET
([16][18])
Requirements QE1. Do you need to find more high-level requirements? Are you looking for ways to prompt Yes. Try: Interactive Approaches: NFR([9]) i*([27][27][33]) Tropos([4][7]) SNET([16][18]) i*
Elicitation
further elicitation?
Metrics([11])
QE2. Do you need to find detailed system requirements?
Yes. Try: Quantitative or Detailed Information: Tropos Probabalistic Satisfaction Analysis
([3][21][22][23]) KAOS Satisfaction Analysis ([31]) GRL Quant. Analysis ([1]) i* Quant. Metrics
([11][12][13]) Tropos Planning ([4][6][7][8]) Tropos Modeling Checking ([8][14][15][19])
SNET([16][17][18][18]) i* Simulation([34])
Objectives
Procedures
QE3. Do you need to consider non-functional requirements difficult to quantify?
Requirements
Improvement
Design
Yes. Try: Approaches supporting softgoals or contributions: NFR([9]) i* Satisfaction Analysis
([26][27][33]) Tropos Satisfaction Analysis ([3][21][22][23]) Tropos Model Checking([14][15])
GRL([1]) i* Metrics([11][12][13]) SNET([16][17][18])
QE4. Do you need to capture domain assumptions?
Yes. Try: Approaches using Satisfaction Arguments: i* Satisfaction Arguments [33]
QR1. Are you working with a system where safety/security/ privacy/risks or other specific Yes. Try: Analysis over Specific Constructs or Metric Approaches: KAOS([31]) i*
properties are critical considerations?
Metrics([11][12][13]) AGORA([30]) Tropos Risk, Trust, and Security([3][4] [8][19]) SNET
Trust([17])
QR2. Do you need to find errors and inconsistencies in requirements?
Yes. Try: Model Checking: Tropos([8][14][15][19]) SNET([16][18])
QD1. Are you aware of a sufficient number of high-level design alternatives?
No. Try: Agent, Planning, Forward and Backward Satisfaction Approaches: NFR([9]) i*
Satisfaction Analysis ([26][27][33]) Tropos Planning([4][6][7][8]) KAOS([31]) GRL Forward
Satisfaction Analysis([1]) SNET Planning([16][18])
QD2. Are you aware of a sufficient number of detailed design alternatives?
No. Try: Quantitative Planning, Forward and Backward Satisfaction Approaches: KAOS
Satisfaction Analysis ([31]) GRL Forward Satisfaction Analysis([1]) Tropos Planning([6][7])
SNET Planning([16][18])
QD3. Do you need to evaluate and choose between high-level design alternatives?
Yes. Try: Satisfaction Analysis, Metrics and Agent Approaches: KAOS Satisfaction
Analysis([31]) i* Forward Satisfaction([26][33]) GRL Satisfaction Analysis([1]) i*
Metrics([11][12][13]) Tropos Risk([4])
QD4. Do you need to evaluate and choose between detailed design alternatives?
Yes. Try: Quantitative or Detailed Information: Tropos Probabalistic Satisfaction Analysis
([3][21][22][23]) KAOS Satisfaction Analysis ([31]) GRL Quant. Analysis ([1]) i* Quant. Metrics
([11][12][13]) Tropos Planning ([4][6][7][8]) Tropos Modeling Checking ([8][14][15][19])
SNET([16][17][18][18]) i* Simulation([34])
QD5. Do you need to find acceptable processes?
QD6. Do you need to test run-time operation before implementation?
Yes. Try: Planning Approaches: Tropos Planning([4][6][7][8]) SNET Planning([16][18])
Yes. Try: Simulation Approaches: SNET([16][17][18]) i* Simulation([34])
Analyzing Goal Models, Horkoff & Yu
24
Guideline Usage Examples

Example: Online Counseling Domain (Horkoff & Yu, 2009)
 Online counseling alternatives: text messaging or chat room?
 Apply guiding questions…





High degree of social interaction (QU1)
Do no yet understand details, not yet confident in the accuracy and
completeness of models (Qu2, QM1)
Communication is important, scoping is challenging (QS1, QU2)
Etc…
Recommendations:



Interactive, agent-oriented techniques for forward satisfaction
analysis supporting softgoals
Analysis for anonymity or privacy with the same techniques or with
GRL Satisfaction Analysis, and/or i* Metrics
If the required detailed information is available, apply planning
and/or simulation techniques
Analyzing Goal Models, Horkoff & Yu
25
Conclusions



First step towards making goal model analysis techniques
more accessible to modelers
Enable potential users to user their knowledge of the
domain and analysis objectives to select one or more
procedures
We have attempted to be neutral in our analysis


Each procedure has unique abilities
Future work is needed to undertake studies to validate or
refute the claims made by our guidelines

Hope that guidelines will be expanded and refined as more
application experiences are available
Analyzing Goal Models, Horkoff & Yu
26
Thank you

www.cs.utoronto.ca/~jenhork


jenhork@cs.utoronto.ca
www.cs.utoronto.ca/~eric

yu@ischool.utoronto.ca
Analyzing Goal Models, Horkoff & Yu
27
Outline









Goal Models
Goal Model Analysis
Motivation: Abundance of Approaches
Survey of Goal Model Analysis Approaches
Survey Results Summary
Objectives of Goal Model Analysis
Mapping of Procedures to Objectives
Example Selection
Conclusions & Future Work
Analyzing Goal Models, Horkoff & Yu
28
Survey of GORE Analysis Techniques: Selection

Article selection:
 Started with a set of known relevant papers



Alternative selection methods:




Linked work through references
Stopped with picture of breadth was captured (24 papers)
Search for specific key words…
… in specific journals, conferences, portals
… during specific time periods
Challenge: work in goal model analysis appears in a range of venues with a
range of keywords


Venues: Books, RE, REJ, Agent-related conferences, CAiSE, AI related journal, FSE,
PoEM, Journal of Information Systems, Trust-related Conference, ASE, etc…
Keywords: agent-oriented software development, goal-oriented requirements
analysis, early requirements analysis, multi-agent systems, agent-oriented software
engineering, agent-oriented methodologies, risk analysis, countermeasure
identification, goal modeling, goal-oriented analysis, quality metrics, etc….
Analyzing Goal Models, Horkoff & Yu
29
Guideline Usage Examples

Example: Wireless service from Amyot et al. 2010



New wireless service added to existing network
Where should the data and service be located?
Apply guiding questions…






Domain contains some interacting systems (QU1), no emphasis on
communication (QC1)
Aware of alternatives, but need to select one (QD1, QD3)
Do not yet understand details, detailed alternatives, don’t have access to
specific information, don’t want to find processes or perform simulations
(QU2, QR2, QD2, QD5, QD6)
Domain is well understood, scope is clear, models are sufficiently complete
(QE1, QS1, QM1)
Must consider non-functional requirements (QE3), data privacy (QR1)
Recommendations:


Agent-oriented approaches supporting softgoals to consider social
and non-functional nature of the problem
Satisfaction analysis or metrics to chose between alternatives
Analyzing Goal Models, Horkoff & Yu
30
Guideline Usage Examples

Example: Online Counseling Domain from Horkoff & Yu, 2009


Online counseling alternatives: text messaging or chat room?
Apply guiding questions…







High degree of social interaction (QU1)
Do no yet understand details, not yet confident in the accuracy and
completeness of models (Qu2, QM1)
Communication is important, scoping is challenging (QS1, QU2)
Consider many non-functional requirements, privacy and security especially,
capture assumptions (QE3, QE4, QR1)
Need to find and evaluate alternatives (QD1, QD3)
Could be useful to find the most successful process (plan) for counseling or
simulate throughput (QD4, QD5)
Recommendations:



Interactive, agent-oriented techniques for forward satisfaction analysis
supporting softgoals
Analysis for anonymity or privacy with the same techniques or with GRL
Satisfaction Analysis, and/or i* Metrics
If the required detailed information is available, apply planning and/or
simulation techniques
Analyzing Goal Models, Horkoff & Yu
31