detection of seed methods for quantification of feature confinement
TRANSCRIPT
Detection of Seed Methods for Quantification of Feature Confinement
Andrzej Olszak, Eric Bouwers,
Bo Nørregaard Jørgensen, Joost Visser
Agenda
I. Features in OO programsII. Feature-centric quality assessmentIII. Our approach
I. Seed methodsII. Automated detection of seed methods
IV. EvaluationV. Evolutionary applicationVI. Conclusion
— 2 —
Features
• Feature is a unit of user functionality– Bridges the problem and solution domains
– Unit of evolutionary change
Feature specs Source code
Features in OO programs
• Crosscutting inter-class collaborations• Implicit boundaries
Feature specs Source code
Features in OO programs
• Crosscutting inter-class collaborations• Implicit boundaries
Feature specs Source code
Features in OO programs
• Crosscutting inter-class collaborations• Implicit boundaries• More scattering = bigger scope of change
scattering
Feature specs Source code
Features in OO programs
• Crosscutting inter-class collaborations• Implicit boundaries• More scattering = bigger scope of change• More tangling = more change propagation
scattering
tangling
Feature-centric quality assessment
• Scaling quantification of feature confinement:– Hundreds of large-scale industrial systems
– Mixed availability of documentation, issue tracker tickets, full revision histories…
– System-level ratings
• Existing feature location approaches are insufficient and needlessly fine-grained
Pro
gra
m
Swin
g
Our approach – seed methods
• Features as user-triggered control flows
• Seed methods are the starting points of feature control graphs
…
Feature Seed MethodactionPerformed()
:ActionListener :Model:JButton
Our approach – automated detection of seed methods
• Automated detection of seeds for static slicing– Anonymous seed methods
• Rank methods Product of two simple heuristics:– Method with popular names
• Exploits regularity, i.e. polymorphism-based callbacks
– Methods that yield large static control-flow slices• Filters uninteresting shallow methods, e.g. toString, getID
• Take δ=10 best methods*
Automated detection of seed methods - example
Automated detection of seed methods - example
Automated detection of seed methods - example
Automated detection of seed methods - example
2/10
Automated detection of seed methods - example
2/10
Automated detection of seed methods - example
2/10
Automated detection of seed methods - example
2/10
Automated detection of seed methods - example
2/10
avg(5/10, 4/10)
Automated detection of seed methods - example
2/10
avg(5/10, 4/10)
0.2 * 0.45
A more realistic example:JHotDraw 7.6
keyPressed
actionPerformed
getPresentationName
wrapViewComponent
Evaluation
• Hypothesis: – Traces produced by our approach cover the same
regions of source code as a ground truth
• Ground-truth slices based on manually constructed set of seed methods:– 14 medium and large Java programs
– Based on used interfacing technologies, e.g. Swing, SWT, Android, Spring
Ground truth
Results
• Average results:
• Interesting cases:
Intersection79%
Appr.-only6%
Non-covered13%
GT-only2%
k9mail Checkstyle Spring
process verify
invoke
Application: tracking evolution of feature confinement
• Checkstyle, 10 years, 27 releases: 1.0 – 5.4– Violation detectors are the features
• Scattering and tangling calibrated on 55 systems
Application: tracking evolution of feature confinement
• Checkstyle, 10 years, 27 releases: 1.0 – 5.4– Violation detectors are the features
• Scattering and tangling calibrated on 55 systems
Architectural restructuring
Application: tracking evolution of feature confinement
• Checkstyle, 10 years, 27 releases: 1.0 – 5.4– Violation detectors are the features
• Scattering and tangling calibrated on 55 systems
Architectural restructuring
No erosion despite 2x
growth in LOC
Application: tracking evolution of feature confinement
• Checkstyle, 10 years, 27 releases: 1.0 – 5.4– Violation detectors are the features
• Scattering and tangling calibrated on 55 systems
Architectural restructuring
No erosion despite 2x
growth in LOC
Removal of J2EE detectors,
refactorings
Conclusion
• Seed methods detection:– Detected approximate the ground truth
– We find solution-specific abstractions
• Interesting perspective on software evolution
• Persisting challenges:– Heuristic parameters and some design decisions
(δ=10, product, ranking)
– Comparative validation vs. traditional approaches
Q&A