ICSOFT-EA slides

1. Modularization Compass:
Navigating the White Waters
of Feature-Oriented Modularity
Andrzej Olszak and Bo Nørregaard Jørgensen
The Maersk Mc-Kinney Moller Institute
University of Southern Denmark

2. Agenda
1. Motivation
2. Features
3. Evolution of feature modularity
4. Drift of modularity
5. Evaluation
6. Results

3. Motivation
• Software systems have to evolve to
accommodate new user expectations:
– Add and enhance features
– Fix existing features
• Working with existing code is difficult:
– Where is this feature implemented?
– Why is this logic here?
– Can this change break another feature?
• It only gets more difficult over time!

4. Features – user’s perspective on code
• Feature – unit of user-identifiable functionality of software
– Scattering increases change scope and delocalization effects
– Tangling increases change propagation and interleaving effects

5. Tracking the evolution of features
Add feature Enhance
feature
FSCA=2 (2+2)/2
(3+2)/2
• Are these values low or high?
– Should we aim for FSCA=1? What is the ‘optimal’ value?
• How much can be improved by refactoring?
– When to refactor?
– What/how to refactor?
?!

6. Drift of modularity as distance to ‘optimum’
• Drift is the distance between the actual and
the ‘optimal’ values of a metric
– ‘optimal’ = after applying optimal refactorings*
Actual modularization
Optimal modularization
r1 r2 r3 r4
Absolute
value
Drift
– Distance from optimal design
– Potential for refactoring

7. Calculating drift
• Obtaining ‘optimal’ modularizations
– Criteria for detecting ‘optimality’
– Efficient traversal of design space
– Automated refactorings

8. Modularization goals
• Feature + object-oriented criteria

9. Calculating drift by optimizing program
structure
• Multi-objective optimization of package structures to
modularize features
– Based on the move-class refactoring
• MOGGA: genetic algorithm on a population of designs
– Pareto-optimality
– Grouping operators
• Resulting designs established using automated code
transformations

10. Example optimization results
• MOGGA config: population of 300 designs over 500 iterations
with 5% mutation prob.
0.03
0.025
0.02
0.015
0.01
0.005
0
fsca
JHotDraw SVG
Scattering -55%
Feature
Original
Automated
0.045
0.04
0.035
0.03
0.025
0.02
0.015
0.01
0.005
0
ftang
JHotDraw SVG
Tangling -60%
Package
Original
Automated

11. Evaluation
• Goal: To evaluate whether drift-based metrics
bring new information, as compared to their
absolute counterparts
• Procedure:
1. Locate features in code
2. Measure of absolute Scattering and Tangling
3. Measure drifts
• Target: release histories of 3 OSS

12. Feature location
• Recovered feature specifications from
documentation and UIs
• Located features in classes and methods using
execution tracing and feature-entry annotations
Application releases Identified features
JHotDraw Pert
11 Releases: 5.2; 5.3;
5.4b1; 6.0b1; 7.0.7; 7.0.8;
7.0.9; 7.1; 7.2; 7.3; 7.3.1
Align, Dependency tool, Edit basic, Edit figure, Exit program, Export drawing
(7.0.7), Group figures, Init program, Line tool (removed in 6.0b1), Modify
figure, Multiple windows (7.0.7), New drawing, Open drawing, Order figures,
Save as drawing, Selection tool, Snap to grid, Task tool, Text tool, Undo redo
(5.3), Zoom (7.0.7)
RText
17 Releases
…
FreeMind
13 Releases
…

13. Results – JHotDraw Pert
• Abs: FSCA  FTANG 
• Drift: FSCA  FTANG 
– FTANG drift > FSCA drift
• Architectural refactoring in v7.0.7
– Improved separation of features
– FSCA: drift decreases while absolute
value increases
• Abs: FSCA  FTANG
• Drift: FSCA FTANG
– FSCA drift remains constant while
absolute value increases
9
8
7
6
5
4
3
2
1
0
FSCA
Release
Scattering drift of Pert
14
12
10
8
6
4
2
0
FTANG
Release
Tangling drift of Pert
Drift of tangling Absolute tangling

14. Some trends in remaining results
“Breakaway” points
“Oscillations” of drift?
(Antón and Potts, 2003)
Tangling drift >
scattering drift
(potential for separating)
7
6
5
4
3
2
1
20
18
16
14
12
10
8
6
4
2
0
FTANG
Release
Tangling drift of FreeMind
Drift of tangling Absolute tangling
10
9
8
7
6
5
4
3
2
1
12
10
8
6
4
2
0
FTANG
Release
Tangling drift of RText
Drift of tangling Absolute tangling
0
FSCA
Release
Scattering drift of RText
Drift of scattering Absolute scattering
0
FSCA
Release
Scattering drift of FreeMind
Drift of scattering Absolute scattering

15. Conclusion & Next steps
• Drift:
– Distance from ‘optimum’
– Potential for refactoring
– Not limited to feature-oriented metrics!
• Interesting observations from the 3 systems
– Demonstrated usefulness, more data needed to fully
understand and generalize the observations
• MOGGA config & performance can be optimized
• Method-level refactorings can be explored

16. Questions?