The Conceptual Coupling Metrics for Object-Oriented Systems

1
The Conceptual Coupling Metrics for
Object-Oriented Systems

• Denys Poshyvanyk and Andrian Marcus
• SEVERE group _at_
• 22nd IEEE International Conference on Software
  Maintenance
• Philadelphia, Pennsylvania
• September 27, 2006

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Motivation

• Concepts and classes
• Implementation and representation of concepts
• Semantic information

3
Example

• Methods from MySecMan class in Mozilla

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Approach

• Latent Semantic Indexing
• Advantages
• captures essential semantic info via
  dimensionality reduction
• overcomes problems with polysemy and synonymy
• easy to apply on the source code

5
Related Work

• Coupling measures
• Previously solved problems
• Traceability link recovery
• Managing software artifacts
• Conceptual cohesion
• Software clustering
• Concept/feature location
• Requirements traceability
• Isolating concerns in requirements

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Extracting Semantic Info

• Source code -gt Corpus (doc method)
• Preprocessing split_identifiers
  SplitIdentifiers
• Vector space term-by-document matrix
• Singular Value Decomposition -gt LSI subspace

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Computing Conceptual Similarity

• Cosine between vectors

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Conceptual Coupling between Classes

• Method - Class conceptual similarity
• Class - Class conceptual similarity

Conceptual coupling between A and B 0.4
Class A
Class B
0.5
method1
method1
0.6
0.5
0.2
0.7
method2
method2
0.4
0.3
0.4
0.3
0.2
0.4
method3
method3
0.3
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Maximal Conceptual Coupling

• Conceptual coupling based on the strongest
  conceptual coupling link

Conceptual coupling between A and B 0.56
Class A
Class B
0.5
method1
method1
0.6
0.7
0.2
0.7
method2
method2
0.4
0.3
0.6
0.3
0.2
0.4
method3
method3
0.4
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Are We Measuring Anything New?

• Compare with other coupling measures
• Coupling between classes (CBO) Chidamber04
• Response for class (RFC) Chidamber04
• Message passing coupling (MPC) Li93
• Data abstraction coupling (DAC) Li93
• Information-flow based coupling (IPC) Lee95
• A suite of coupling measures by Briand et al
  ACAIC, OCAIC, ACMIC and OCMIC
• Tools
• Columbus Ferenc04
• IRC2M

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Software Systems

• Ten open-source systems from different domains

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Principal Component Analysis

• Identifying groups of metrics (variables) which
  measure the same underlying mechanism that
  defines coupling (dimension)
• PCA procedure
• collect data
• identify outliers
• perform PCA

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PCA Results Rotated Components

• CoCC and CoCCm define new dimensions (PC2
  and PC6)

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Discussion of the Results

• Conceptual similarities between all pairs of
  classes
• Selected classes with highest values of
  conceptual coupling
• No direct structural dependencies

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Discussion of the Results Cont.

• Concepts
• TortoiseCVS merge and update CVS operations
• WinMerge checking out a revision of the file
• Related concepts and history of common changes

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Current Future Work

• Connection to change/fault proneness
• Impact analysis
• Hidden dependencies/indirect coupling
• Aspect mining
• Refining canonical feature sets
• Concept location and clustering