Physical and Conceptual Identifier Dispersion: Measures and Relation to Fault Proneness
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Physical and Conceptual Identifier Dispersion: Measures and Relation to Fault Proneness
- 1. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Physical and Conceptual Identifier Dispersion: Measures and Relation to Fault Proneness Venera Arnaoudova Laleh Eshkevari Rocco Oliveto Yann-Ga¨el Gu´eh´eneuc Giuliano Antoniol SOCCER Lab. – DGIGL, ´Ecole Polytechnique de Montr´eal, Qc, Canada SE@SA Lab – DMI, University of Salerno - Salerno - Italy Ptidej Team – DGIGL, ´Ecole Polytechnique de Montr´eal, Qc, Canada September 15, 2010 SOftware Cost-effective Change and Evolution Research Lab Software Engineering @ SAlerno Pattern Trace Identification, Detection, and Enhancement in Java
- 2. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Outline Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work 2 / 16
- 3. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Introduction Fault identification size (e.g., [Gyim´othy et al., 2005]) cohesion (e.g., [Liu et al., 2009]) coupling (e.g., [Marcus et al., 2008]) number of changes (e.g., [Zimmermann et al., 2007]) Importance of linguistic information program comprehension (e.g., [Takang et al., 1996, Deissenboeck and Pizka, 2006, Haiduc and Marcus, 2008, Binkley et al., 2009]) code quality (e.g., [Marcus et al., 2008, Poshyvanyk and Marcus, 2006, Butler et al., 2009]) 3 / 16
- 4. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Our study Term dispersion We are interested in studying the relation between term dispersion and the quality of the source code. term basic component of identifiers dispersion the way terms are scattered among different entities (attributes and methods) quality absence of faults Example: What is the impact of using getRelativePath, returnAbsolutePath, and setPath as method names on the fault proneness of those methods? 4 / 16
- 5. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Dispersion measures (1/3) Physical dispersion - Entropy fee foo bar Terms Entities E1 E2 E3 E4 E5 Entropy The circle indicates the occurrences of a term in an entity. The higher the size of the circle the higher the number of occurrences. 5 / 16
- 6. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Dispersion measures (2/3) Conceptual dispersion - Context Coverage E1 E3 E2 E5 E4 C1 C3 C2 C4 Entity Contexts Entity contexts are identified taking into account the terms contained in the entities. fee foo bar Terms ContextsC1 C2 C3 C4 Context coverage The star indicates that the term appears in the particular context. 6 / 16
- 7. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Dispersion measures Aggregated metric - numHEHCC (3/3) Context Coverage Entropy th H th CC 7 / 16
- 8. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Dispersion measures Aggregated metric - numHEHCC (3/3) Context Coverage Entropy th H th CC ? 7 / 16
- 9. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Dispersion measures Aggregated metric - numHEHCC (3/3) Context Coverage Entropy th H th CC H: used in few identifiers CC: used in similar contexts 7 / 16
- 10. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Dispersion measures Aggregated metric - numHEHCC (3/3) Context Coverage Entropy th H th CC ? 7 / 16
- 11. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Dispersion measures Aggregated metric - numHEHCC (3/3) Context Coverage Entropy th H th CC H: used in many identifiers CC: used in similar contexts 7 / 16
- 12. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Dispersion measures Aggregated metric - numHEHCC (3/3) Context Coverage Entropy th H th CC ? 7 / 16
- 13. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Dispersion measures Aggregated metric - numHEHCC (3/3) Context Coverage Entropy th H th CC H: used in few identifiers CC: used in different contexts 7 / 16
- 14. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Dispersion measures Aggregated metric - numHEHCC (3/3) Context Coverage Entropy th H th CC ? 7 / 16
- 15. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Dispersion measures Aggregated metric - numHEHCC (3/3) Context Coverage Entropy th H th CC H: used in many identifiers CC: used in different contexts 7 / 16
- 16. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Dispersion measures Aggregated metric - numHEHCC (3/3) Context Coverage Entropy th H th CC H: used in many identifiers CC: used in different contexts ! 7 / 16
- 17. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Dispersion measures Aggregated metric - numHEHCC (3/3) Context Coverage Entropy th H th CC H: used in many identifiers CC: used in different contexts ! For each entity, numHEHCC counts the number of such terms 7 / 16
- 18. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Our study - refined (1/2) Research question 1 RQ1 – Metric Relevance: Does numHEHCC capture characteristics different from size? Our believe: Yes it does, although we expect some overlap. To this end, we verify the following: 1. To what extend numHEHCC and size vary together. 2. Can size explain numHEHCC? 3. Does numHEHCC bring additional information to size for fault explanation? 8 / 16
- 19. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Our study - refined (2/2) Research question 2 RQ2 – Relation to Faults: Do term entropy and context coverage help to explain the presence of faults in an entity? Our believe: Yes it does! How? 1. Estimate the risk of being faulty when entities contain terms with high entropy and high context coverage. 9 / 16
- 20. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Objects Objects ArgoUML v0.16 – a UML modeling CASE tool. Rhino v1.4R3 – a JavaScript/ECMAScript interpreter and compiler. Program LOC # Entities # Terms ArgoUML 97,946 12,423 2517 Rhino 18,163 1,624 949 We consider as entities both methods and attributes. 10 / 16
- 21. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Case study RQ1 – Metric Relevance (1/3) Results for RQ1 – Metric Relevance To what extend numHEHCC and size vary together? ArgoUML: 40% Rhino: 43% Correlation between numHEHCC and LOC numHEHCC LOC 11 / 16
- 22. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Case study RQ1 – Metric Relevance (2/3) Results for RQ1 – Metric Relevance Can size explain numHEHCC? ArgoUML: 17% Rhino: 19% Composition of numHEHCC. 12 / 16
- 23. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Case study RQ1 – Metric Relevance (3/3) Results for RQ1 – Metric Relevance (cont’d) Does numHEHCC bring additional information to size for fault explanation? Variables Coefficients p-values MArgoUML Intercept -1.688e+00 2e − 16 LOC 7.703e-03 8.34e − 10 numHEHCC 7.490e-02 1.42e − 05 LOC:numHEHCC -2.819e-04 0.000211 MRhino Intercept -4.9625130 2e − 16 LOC 0.0041486 0.17100 numHEHCC 0.2446853 0.00310 LOC:numHEHCC -0.0004976 0.29788 13 / 16
- 24. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Case study Results for RQ2 – Relation to Faults (1/1) The risk of being faulty when entities contain terms with high entropy and high context coverage. All entities 14 / 16
- 25. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Case study Results for RQ2 – Relation to Faults (1/1) The risk of being faulty when entities contain terms with high entropy and high context coverage. All entities 14 / 16
- 26. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Case study Results for RQ2 – Relation to Faults (1/1) The risk of being faulty when entities contain terms with high entropy and high context coverage. All entities numHEHCC 10% of the entities 14 / 16
- 27. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Case study Results for RQ2 – Relation to Faults (1/1) The risk of being faulty when entities contain terms with high entropy and high context coverage. All entities numHEHCC 10% of the entities Risk of being faulty? 14 / 16
- 28. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Case study Results for RQ2 – Relation to Faults (1/1) The risk of being faulty when entities contain terms with high entropy and high context coverage. All entities numHEHCC 10% of the entities Risk of being faulty? ArgoUML: 2 x higher Rhino: 6 x higher 14 / 16
- 29. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Conclusions and future work Conclusions Entropy and context coverage, together, capture characteristics different from size! Entropy and context coverage, together, help to explain the presence of faults in entities! Future directions Replicate the study to other systems. Use entropy and context coverage to suggest refactoring. Study the impact of lexicon evolution on entropy and context coverage. 15 / 16
- 30. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Thank you! Questions? 16 / 16
- 31. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Binkley, D., Davis, M., Lawrie, D., and Morrell, C. (2009). To CamelCase or Under score. In Proceedings of 17th IEEE International Conference on Program Comprehension. IEEE CS Press. Butler, S., Wermelinger, M., Yu, Y., and Sharp, H. (2009). Relating identifier naming flaws and code quality: An empirical study. In Proceedings of the 16th Working Conference on Reverse Engineering, pages 31–35. IEEE CS Press. Deissenboeck, F. and Pizka, M. (2006). Concise and consistent naming. Software Quality Journal, 14(3):261–282. Gyim´othy, T., Ferenc, R., and Siket, I. (2005). Empirical validation of object-oriented metrics on open source software for fault prediction. 16 / 16
- 32. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work IEEE Transactions on Software Engineering, 31(10):897–910. Haiduc, S. and Marcus, A. (2008). On the use of domain terms in source code. In Proceedings of 16th IEEE International Conference on Program Comprehension, pages 113–122. IEEE CS Press. Liu, Y., Poshyvanyk, D., Ferenc, R., Gyim´othy, T., and Chrisochoides, N. (2009). Modelling class cohesion as mixtures of latent topics. In Proceedings of 25th IEEE International Conference on Software Maintenance, pages 233–242, Edmonton, Canada. IEEE CS Press. Marcus, A., Poshyvanyk, D., and Ferenc, R. (2008). Using the conceptual cohesion of classes for fault prediction in object-oriented systems. IEEE Transactions on Software Engineering, 34(2):287–300. 16 / 16
- 33. Physical and Conceptual Identifier Dispersion Venera Arnaoudova, Laleh Eshkevari, Rocco Oliveto, Yann-Ga¨el Gu´eh´eneuc, Giuliano Antoniol Introduction Our study Dispersion measures Our study - refined Case study RQ1 – Metric Relevance RQ2 – Relation to Faults Conclusions and future work Poshyvanyk, D. and Marcus, A. (2006). The conceptual coupling metrics for object-oriented systems. In Proceedings of 22nd IEEE International Conference on Software Maintenance, pages 469 – 478. IEEE CS Press. Takang, A., Grubb, P., and Macredie, R. (1996). The effects of comments and identifier names on program comprehensibility: an experiential study. Journal of Program Languages, 4(3):143–167. Zimmermann, T., Premraj, R., and Zeller, A. (2007). Predicting defects for eclipse. In Proceedings of the Third International Workshop on Predictor Models in Software Engineering. 16 / 16