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The Conceptual Coupling Metrics for Object-Oriented Systems

The Conceptual Coupling Metrics for Object-Oriented Systems. Denys Poshyvanyk and Andrian Marcus SEVERE group @ 22 nd IEEE International Conference on Software Maintenance Philadelphia, Pennsylvania September 27, 2006. Motivation. Concepts and classes

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The Conceptual Coupling Metrics for Object-Oriented Systems

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  1. The Conceptual Coupling Metrics for Object-Oriented Systems Denys Poshyvanyk and Andrian Marcus SEVERE group @ 22nd IEEE International Conference on Software Maintenance Philadelphia, Pennsylvania September 27, 2006

  2. Motivation • Concepts and classes • Implementation and representation of concepts • Semantic information

  3. Example • Methods from MySecMan class in Mozilla

  4. 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

  6. Extracting Semantic Info • Source code -> Corpus (doc = method) • Preprocessing: split_identifiers & SplitIdentifiers • Vector space = term-by-document matrix • Singular Value Decomposition -> LSI subspace

  7. Computing Conceptual Similarity • Cosine between vectors

  8. 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 0.6 method1 0.5 0.2 0.7 method2 0.3 0.4 method2 0.4 0.3 0.2 0.4 method3 method3 0.3

  9. 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 0.6 method1 0.7 0.2 0.7 method2 0.3 0.4 method2 0.6 0.3 0.2 0.4 method3 method3 0.4

  10. Are We Measuring Anything New? • Compare with other coupling measures: • Coupling between classes (CBO) [Chidamber’04] • Response for class (RFC) [Chidamber’04] • Message passing coupling (MPC) [Li’93] • Data abstraction coupling (DAC) [Li’93] • Information-flow based coupling (IPC) [Lee’95] • A suite of coupling measures by Briand et al: ACAIC, OCAIC, ACMIC and OCMIC • Tools: • Columbus [Ferenc’04] • IRC2M

  11. Software Systems • Ten open-source systems from different domains

  12. 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

  13. PCA Results: Rotated Components • CoCC and CoCCm define new dimensions (PC2 and PC6)

  14. Discussion of the Results • Conceptual similarities between all pairs of classes • Selected classes with highest values of conceptual coupling • No direct structural dependencies

  15. 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

  16. Current & Future Work • Connection to change/fault proneness • Impact analysis • Hidden dependencies/indirect coupling • Aspect mining • Refining canonical feature sets • Concept location and clustering

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