Multi-Stakeholder Tensioning Analysis
in Requirements Optimisation
Yuanyuan Zhang
CREST Centre
University College London
Agenda
Background
Problem
Solution
Empirical Study
Conclusion
Requirements Engineering Process
Acquisition
Evolution &
Management
Modelling&
Modelling
&
Analysis
Validation &
Verification
Background
Problem
Specification
Solution
Empirical Study
Conclusion
University College London yuanyuan.zhang@cs.ucl.ac.uk
Requirements Selection &
Optimisation
Task
Using prioritisation, visualisation, and
optimisation techniques helps decision maker
to select the optimal or near optimal subset
from all possible requirements to be
implemented.
Background
Problem
Solution
Empirical Study
Conclusion
University College London yuanyuan.zhang@cs.ucl.ac.uk
Goals
Stakeholder A
Stakeholder
B
Cost
Revenue
Background
Problem
Solution
Empirical Study
Conclusion
University College London yuanyuan.zhang@cs.ucl.ac.uk
Search based Requirements
Optimisation
Use of meta-heuristic algorithms to automate and optimise
requirements analysis process
– Choose appropriate representation of problem
– Define problem specific fitness function (to evaluate
potential solutions)
– Use search based techniques to lead the search towards
optimal points in the solution space
Background
Problem
Solution
Empirical Study
Conclusion
University College London yuanyuan.zhang@cs.ucl.ac.uk
Representation
Stakeholder:
Weight:

C  c1 ,..., c j ,..., cm 

Weight  w1 ,..., w j ,..., wm 
Requirements:
Cost:
Background
R  r1,..., ri ,..., rn 
Cost  cost1,..., costn 
Problem
Solution
Empirical Study
Conclusion
University College London yuanyuan.zhang@cs.ucl.ac.uk
Representation
rii :
• Each stakeholder c j assigns a value to requirements r

value ri , c j 
• Each stakeholder c j has a subset of requirements that expect
to be fulfilled denoted by R j
value  r , c j   0
r  Rj
Rj  R,
• The overall score of a given requirement ri can be calculated
by:
m

scorei   w j  value ri , c j 
j 1
Background
Problem
Solution
Empirical Study
Conclusion
University College London yuanyuan.zhang@cs.ucl.ac.uk
Data Set Generation & Initialisation
Matlab Files
.mat
.dat
Real World Data
Sets
format
input
Requirement.Number
Requirement.Value
Requirement.Cost
Requirement.Dependency
27 Combination
Random Data
Sets
generate
initialise
Other Random
Data Sets
Background
process
Problem
Stakeholder.Number
Stakeholder.Weight
Stakeholder.Subset
format
Requirement-Stakeholder
Matrix.Density
Random.Distribution
Solution
Empirical Study
Conclusion
University College London yuanyuan.zhang@cs.ucl.ac.uk
Requirements Selection Process
Basic Value/Cost
NSGA-II
ANOVA Analysis
Archive-based
NSGA-II
Today/Future
Importance
Value/Cost Trade-off Analysis
Two-Achieve
Fitness-Invariant
Dependency
Spearman’s
Rank Correlation
Pareto GA
Single
Objective GA
Greedy*
Fitness-Affecting
Dependency
Interaction Management
Tensioning Analysis
Iterate
No
Fairness Analysis
Multi-Stakeholder Analysis
Data Sets Regeneration
Yes
?
Random*
Search Algorithms
Statistic Analysis
Chang
e?
No
Yes
* Strictly speaking, these are not search algorithms.
Result Representation and
Visualisation
2D and 3D Pareto
Fronts
Requirements
Subsets for
Release Planning
Kiviat Diagrams
Insight
Characteristic of
Data Sets
represent &
visualise
Performance of
the Algorithms
Problem
communicate
& feedback
Convergence
Diversity
Results
Background
Marked Line
Charts
Solution
Empirical Study
Conclusion
King’s College London yuanyuan.zhang@kcl.ac.uk
Visualisation
Pareto Optimal Front
Background
Problem
Solution
Empirical Study
Conclusion
University College London yuanyuan.zhang@cs.ucl.ac.uk
Fitness Functions
The problem is to select a set of requirements that maximise the
total value to each stakeholder, which is expressed as a percentage.
The model of fitness functions represented as:


n
i 1
Maximise
value(ri , c j )  xi
rR j
n
subject to
 cost
i 1
Background
Problem
i
Solution
value(r , c j )
 B,
B0
Empirical Study
Conclusion
University College London yuanyuan.zhang@cs.ucl.ac.uk
Data Sets Used
1. Motorola Data Set:
35 Requirements and 4 Stakeholders
2. Greer and Ruhe Data Set:
20 Requirements and 5 Stakeholders
Background
Problem
Solution
Empirical Study
Conclusion
King’s College London yuanyuan.zhang@kcl.ac.uk
Data Sets Used
3. 27 Combination Levels of Random Data Sets:
the No. of requirements
the No. of stakeholders
the density of the
stakeholder-requirement
matrix
Background
Problem
Solution
Empirical Study
Conclusion
King’s College London yuanyuan.zhang@kcl.ac.uk
Multi-Stakeholder Kiviat Diagram
Sta. 1
100%
75%
50%
25%
Sta. 4
0%
Sta. 2
Sta. 3
Background
Problem
Solution
Empirical Study
Conclusion
University College London yuanyuan.zhang@cs.ucl.ac.uk
Multi-Stakeholder Tensioning Analysis
Motorola data set
30% Budgetary Resource Constraint
Background
Problem
Solution
70%
Empirical Study
Conclusion
University College London yuanyuan.zhang@cs.ucl.ac.uk
Solutions on the Pareto Front
Average Solutions
Tensions between the Stakeholders’ Satisfaction for
Different Budgetary Resource Constraints
Multi-Stakeholder Tensioning Analysis
Greer and Ruhe data set
30% Budgetary Resource Constraint
Background
Problem
Solution
70%
Empirical Study
Conclusion
University College London yuanyuan.zhang@cs.ucl.ac.uk
Algorithms’ Performance

C
N
i 1
di
N
num
P
NUM
Background
Problem
Solution
Empirical Study
Conclusion
University College London yuanyuan.zhang@cs.ucl.ac.uk
Algorithms’ Performance
1 . The diversity of the Two-archive algorithm is
significant in most cases
2. The Two-archive and NSGA-II algorithms always
have a better convergence than the Random Search
3. The Two-Archive algorithm outperforms NSGA-II
and Random Search in terms of convergence in
some case
Background
Problem
Solution
Empirical Study
Conclusion
University College London yuanyuan.zhang@cs.ucl.ac.uk
Conclusion
• Present the concept of multi-stakeholder tensioning in
requirements analysis
• Treat each stakeholder as a separate objective to maximise
the requirement satisfaction
• Present the empirical study and statistical analysis
• Provide a simple visual method to show the tensioning
http://www.sebase.org/sbse/publications
University College London yuanyuan.zhang@cs.ucl.ac.uk