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BackgroundBackground
Work on the detection of bad smells
Idea is that when something smells bad, it should be changed
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Open IssuesOpen Issues
Identify factors that cause these smells to guide engineering efforts for : Prevention
Detection
Correction
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How to Track SmellsHow to Track Smells
Use a nose
Follow the evolution of a smell
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Overview of the PresentationOverview of the Presentation
Nose
Systems
Analysis
Results
Thoughts on prevention and correction
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The Nose
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The Nose The Nose In two slides (1)In two slides (1)
We built one in previous work [QSIC2009]
Works with God classes
Probability of a God class given symptoms: Cohesion
Naming
Size
Presence of data classes
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Nose Nose In Two Slides (2)In Two Slides (2)
Size Lexical Little Cohesion
Deals with data classes
Large Controller Class?
God class?
MAX MAX MAX MAX
47%
Conditional probabilities
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Systems
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Xerces-J Eclipse JDT
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System statisticsSystem statistics
Xerces-J Eclipse JDT
10-15% god classes
70% start as god classes
30% of god classes are deleted
2% god classes
61% start as god classes
19% of god classes are deleted
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Analysis
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3 Step Program 3 Step Program -- AnalysisAnalysis
Find patterns in the evolution of the smell
Label these patterns
Analyse the commonalities of these patterns
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DataData02/08/10 13WCRE2009 - S. Vaucher et al.
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TimeTime--independent Analysisindependent Analysis
Use DTW to compare time-independent signals
Apply clustering to discover patterns
Apply classification to group and analyse
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Finding PatternsFinding Patterns
Hierarchical agglomerative clustering using DTW as a distance measure
Constant (smelly)
Gradualimprovement
Sharpimprovement
Gradualdegradation
Sharpdegradation
TemporaryRelief
Temporarybadness
Time
Sig
nal
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Classifying PatternsClassifying Patterns02/08/10 16WCRE2009 - S. Vaucher et al.
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Pattern DistributionPattern Distribution
Xerces-J Eclipse JDT
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Constant PatternConstant Pattern
~65% are always smelly
Questions: Quick and dirty design or willful decision? Look for signs of design effort
Ask the developers
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Constant Pattern Constant Pattern -- ResultsResults
Xerces main developer mentioned that problems were complex and required complex design
Similarly, 82% of classes play a role in a design pattern
The code is not a hack
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DegradationDegradation
~18% of smelly classes were okay initially
In most cases, sharp degradation occurs when developers add data classes to already large class
Degradation always requires new code to be added
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Degradation Degradation growth trends in growth trends in
XercesXerces
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ImprovementsImprovements
More classes are removed than are corrected (30% in Xerces)
Reason justifying the study of antipatterns is to find some standard solutions
Were they used?
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Improvements Improvements results results
Eclipse, lots of added responsibility to data classes (rebalancing)
Xerces, few textbook refactorings
Not obvious since design correction often occurs with enhancements
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PreventionPrevention
Rule mining:
When large amounts of code are added to a class, the probability of it being a god class increases
Inputs: Use code/method change metrics (added/removed/modified)
Current state (clean, borderline, smelly)
Output: Increase, stable, decrease probability of god class
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Rules Rules 02/08/10 WCRE2009 - S. Vaucher et al. 25
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Correcting god classesCorrecting god classes
Gap between theoretical refactorings and their applications.
Too little cohesion (LCOM5): should lead to an extract class refactoring, but almost never used (and metric change not reflected)
Responsibility distribution: move methods. Done more regularly, but almost never from god to data class
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What was saidWhat was said
Methodology to track smells in systems
Discussed the introduction and removal of smells in two open-source systems
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What to rememberWhat to remember
Most god classes tends to be god classes from the start and do not improve
More deletions than improvements
Improvements are hard to classify
Degradation is the result of significant growth (>100%), or because responsibilities are unbalanced
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et al.