An empirical study on how developers reason about module cohesion

ABSTRACT

Several cohesion metrics have been proposed to support development and maintenance activities. The most traditional ones are the structural cohesion metrics, which rely on structural information in the source code. For instance, many of these metrics quantify cohesion based how methods and attributes are related to each other within a given module. Recently, conceptual cohesion metrics have been proposed for computing cohesion based on the responsibilities a given module realizes. Besides different flavors of cohesion, there is a lack of empirical evidence about how developers actually perceive cohesion and what kind of cohesion measurement aligns with developers' perception. In this paper we fill this gap by empirically investigating developers opinion through a web-based survey, which involved 80 participants from 9 countries with different levels of programming experience. We found that: most of the developers are familiar with cohesion; and they perceive cohesion based on class responsibilities, thus associating more with conceptual cohesion measurement. These results support the claim that conceptual cohesion seems to be more intuitive and closer to the human-oriented view of software cohesion. Moreover, the results showed that conceptual cohesion measurement captures the developers' notion of cohesion better than traditional structural cohesion measurement.

References

1. G. Bavota, B. Dit, R. Oliveto, M. Di Penta, D. Poshyvanyk, and A. De Lucia. An empirical study on the developers' perception of software coupling. In Proceedings of the 2013 International Conference on Software Engineering, ICSE '13, pages 692--701, Piscataway, NJ, USA, 2013. IEEE Press.
2. J. M. Bieman and B.-K. Kang. Cohesion and reuse in an object-oriented system. SIGSOFT Softw. Eng. Notes, 20 (SI): 259--262, Aug. 1995.
3. }briand:1998L. C. Briand, J. W. Daly, and J. Wüst. A unified framework for cohesion measurement in object-orientedsystems. Empirical Softw. Engg., 3 (1): 65--117, July 1998.
4. R. P. Buse and W. R. Weimer. Learning a metric for code readability. IEEE Transactions on Software Engineering, 36 (4): 546--558, 2010.
5. S. R. Chidamber and C. F. Kemerer. A metrics suite for object oriented design. IEEE Trans. Softw. Eng., 20 (6): 476--493, June 1994.
6. S. Counsell, S. Swift, A. Tucker, and E. Mendes. Object-oriented cohesion subjectivity amongst experienced and novice developers: an empirical study. SIGSOFT Softw. Eng. Notes, 31 (5): 1--10, Sept. 2006.
7. nez, Anquetil, Delamaro, and de Camargo}durelli:2013R. S. Durelli, D. S. M. Santibánez, N. Anquetil, M. E. Delamaro, and V. V. de Camargo. A systematic review on mining techniques for crosscutting concerns. In Proceedings of the 28th Annual ACM Symposium on Applied Computing, SAC '13, pages 1080--1087, New York, NY, USA, 2013. ACM.
8. L. Etzkorn and H. Delugach. Towards a semantic metrics suite for object-oriented design. In Proceedings of the Technology of Object-Oriented Languages and Systems, TOOLS '00, pages 71--, Washington, DC, USA, 2000. IEEE Computer Society.
9. J. Feigenspan, C. Kästner, J. Liebig, S. Apel, and S. Hanenberg. Measuring programming experience. In Proceedings of the 20th International Conference on Program Comprehension (ICPC), pages 73--82, Los Alamitos, CA, 2012.
10. N. E. Fenton and S. L. Pfleeger. Software Metrics: A Rigorous and Practical Approach. PWS Publishing Co., Boston, MA, USA, 2nd edition, 1998.
11. E. Figueiredo, N. Cacho, C. Sant'Anna, M. Monteiro, U. Kulesza, A. Garcia, S. Soares, F. Ferrari, S. Khan, F. Castor Filho, and F. Dantas. Evolving software product lines with aspects: an empirical study on design stability. In Proceedings of the 30th International Conference on Software engineering, ICSE '08, pages 261--270, New York, NY, USA, 2008. ACM.
12. E. Figueiredo, C. Sant'Anna, A. Garcia, and C. Lucena. Applying and evaluating concern-sensitive design heuristics. Journal of Systems and Software, 85 (2): 227--243, 2012.
13. J. L. Fleiss. Measuring Nominal Scale Agreement Among Many Raters. Psychological Bulletin, 76 (5): 378--382, 1971.
14. A. Garcia, C. Sant'Anna, E. Figueiredo, U. Kulesza, C. Lucena, and A. von Staa. Modularizing design patterns with aspects: a quantitative study. In Proceedings of the 4th international conference on Aspect-oriented software development, AOSD '05, pages 3--14, New York, NY, USA, 2005. ACM.
15. K. Gwet. Handbook of Inter-Rater Reliability (3rd Edition): The Definitive Guide to Measuring the Extent of Agreement Among Multiple Raters. Advanced Analytics Press, 3 edition, 2012.
16. B. Henderson-Sellers. Software Metrics. Prentice Hall, UK, 1996.
17. M. Hitz and B. Montazeri. Measuring coupling and cohesion in object-oriented systems. In Proc. Intl. Sym. on Applied Corporate Computing, 1995.
18. B. Katzmarski and R. Koschke. Program complexity metrics and programmer opinions. In Proc. 20th IEEE International Conference on Program Comprehension, pages 17--26. IEEE, 2012.
19. A. G. Koru and J. J. Tian. Comparing high-change modules and modules with the highest measurement values in two large-scale open-source products. IEEE Trans. Softw. Eng., 31: 625--642, 2005.
20. J. R. Landis and G. G. Koch. The Measurement of Observer Agreement for Categorical Data. Biometrics, 33 (1): 159--174, Mar. 1977.
21. Y. S. Lee and B. S. Liang. Measuring the coupling and cohesion of an object-oriented program based on information flow. In Prof. Intl. Conference on Software Quality, 1995.
22. Y. Liu, D. Poshyvanyk, R. Ferenc, T. Gyimothy, and N. Chrisochoides. Modeling class cohesion as mixtures of latent topics. In 25th IEEE International Conference on Software Maintenance, September 20--26, 2009, Edmonton, Alberta, Canada, pages 233--242. IEEE, 2009.
23. M. V. Mäntylä and C. Lassenius. Drivers for software refactoring decisions. In Proceedings of the 2006 ACM/IEEE international symposium on Empirical software engineering, ISESE '06, pages 297--306, New York, NY, USA, 2006. ACM.
24. A. Marcus and D. Poshyvanyk. The conceptual cohesion of classes. In Proceedings of the 21st IEEE International Conference on Software Maintenance, ICSM '05, pages 133--142, Washington, DC, USA, 2005. IEEE Computer Society.
25. A. Marcus, D. Poshyvanyk, and R. Ferenc. Using the conceptual cohesion of classes for fault prediction in object-oriented systems. IEEE Transactions on Software Engineering, 34: 287--300, 2008.
26. R. C. Martin. Agile Software Development: Principles, Patterns, and Practices. Prentice Hall PTR, Upper Saddle River, NJ, USA, 2003.
27. T. Punter, M. Ciolkowski, B. Freimut, and I. John. Conducting on-line surveys in software engineering. In Proceedings of the 2003 International Symposium on Empirical Software Engineering, ISESE '03, pages 80--, Washington, DC, USA, 2003. IEEE Computer Society.
28. M. P. Robillard and G. C. Murphy. Representing concerns in source code. ACM Trans. Softw. Eng. Methodol., 16 (1), Feb. 2007.
29. C. B. Seaman. Qualitative methods in empirical studies of software engineering. IEEE Trans Softw. Eng., 25: 557--572, 1999.
30. F. Shull, J. Singer, and D. I. K. Sjberg. Guide to Advanced Empirical Software Engineering. Springer Publishing Company, Incorporated, 1st edition, 2010.
31. B. Silva. Developers reasoning about module cohesion: companion website, Oct. 2013. URL http://homes.dcc.ufba.br/textasciitilde brunocs/CohesionSurveyResults.html.
32. B. Silva, C. Sant'Anna, C. Chavez, and A. Garcia. Concern-based cohesion: Unveiling a hidden dimension of cohesion measurement. In Proc. 20th IEEE International Conference on Program Comprehension, pages 103--112. IEEE, 2012.
33. E. Smith, R. Loftin, E. Murphy-Hill, C. Bird, and T. Zimmermann. Improving developer participation rates in surveys. In Proceedings of the 6th International Workshop on Cooperative and Human Aspects of Software Engineering, May 2013.
34. W. P. Stevens, G. J. Myers, and L. L. Constantine. Structured design. IBM Systems Journal, 13 (2): 115 --139, 1974.

Index Terms

• Software and its engineering

  • Software creation and management

    • Software development techniques

      • Reusability

        • Software product lines