Skip to main content
SpringerLink
Log in

Analysis of high structural class coupling in object-oriented software systems

  • Published:
Computing Aims and scope Submit manuscript

Abstract

Understanding coupling between classes in object-oriented (OO) software systems is useful for a variety of software development and maintenance activities. In this paper we propose a novel, network-based methodology to analyze high structural class coupling in OO software systems. The proposed methodology is based on statistically robust structural analysis of class collaboration networks whose nodes are enriched with both software metrics and domain-independent metrics used in analysis of complex networks. To demonstrate the usefulness of the methodology we analyze five open-source, large-scale software systems written in Java. Contrary to frequently reported findings, the obtained results indicate that high structural class coupling in real software systems cannot be accurately modeled by power-law distributions. Our analysis also shows that highly-coupled classes tend to be significantly more voluminous and functionally important compared to loosely coupled classes, and do not tend to be localized in class inheritance hierarchies. Finally, in four out of five analyzed systems highly coupled classes tend to have drastically higher afferent than efferent coupling. This implies that the existence of high class coupling in an OO software system would rather indicate negative aspects of extensive internal class reuse than negative aspects of extensive internal class aggregation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Notes

  1. http://cran.r-project.org/web/packages/poweRlaw/index.html.

References

  1. Albert R, Barabási AL (2002) Statistical mechanics of complex networks. Rev Mod Phys 74(1):47–97. doi:10.1103/RevModPhys.74.47

    Article  MathSciNet  MATH  Google Scholar 

  2. Barabasi AL, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512. doi:10.1126/science.286.5439.509

    Article  MathSciNet  MATH  Google Scholar 

  3. Baxter G, Frean M, Noble J, Rickerby M, Smith H, Visser M, Melton H, Tempero E (2006) Understanding the shape of Java software. In: Proceedings of the 21st annual ACM SIGPLAN conference on object-oriented programming systems, languages, and applications, OOPSLA ’06, pp 397–412. ACM, New York, NY, USA . doi:10.1145/1167473.1167507

  4. Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang D (2006) Complex networks: structure and dynamics. Phys Rep 424(45):175–308. doi:10.1016/j.physrep.2005.10.009

    Article  MathSciNet  MATH  Google Scholar 

  5. Briand LC, Daly JW, Wüst JK (1999) A unified framework for coupling measurement in object-oriented systems. IEEE Trans Softw Eng 25(1):91–121

    Article  Google Scholar 

  6. Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput Netw ISDN Syst 30(1–7):107–117

    Article  Google Scholar 

  7. Chidamber SR, Kemerer CF (1994) A metrics suite for object oriented design. IEEE Trans Softw Eng 20(6):476–493. doi:10.1109/32.295895

    Article  Google Scholar 

  8. Clauset A, Shalizi C, Newman M (2009) Power-law distributions in empirical data. SIAM Rev 51(4):661–703. doi:10.1137/070710111

    Article  MathSciNet  MATH  Google Scholar 

  9. Concas G, Marchesi M, Pinna S, Serra N (2007) Power-laws in a large object-oriented software system. IEEE Trans Softw Eng 33(10):687–708

    Article  Google Scholar 

  10. de Moura APS, Lai YC, Motter AE (2003) Signatures of small-world and scale-free properties in large computer programs. Phys Rev E 68(1):017,102. doi:10.1103/PhysRevE.68.017102

    Article  Google Scholar 

  11. Erceg-Hurn DM, Mirosevich VM (2008) Modern robust statistical methods: an easy way to maximize the accuracy and power of your research. Am Psychol 63(7):591–601. doi:10.1037/0003-066X.63.7.591

    Article  Google Scholar 

  12. Erdös P, Rényi A (1959) On random graphs, I. Publ Math Debr 6:290–297

    MATH  Google Scholar 

  13. Freeman LC (1977) A set of measures of centrality based on betweenness. Sociometry 40:35–41

    Article  Google Scholar 

  14. Hylland-Wood D, Carrington D, Kaplan S (2006) Scale-free nature of Java software package, class and method collaboration graphs. Technical report, TR-MS1286, MIND Laboratory, University of Maryland, College Park, USA

  15. Jenkins S, Kirk SR (2007) Software architecture graphs as complex networks: a novel partitioning scheme to measure stability and evolution. Inf Sci 177:2587–2601. doi:10.1016/j.ins.2007.01.021

    Article  Google Scholar 

  16. Kósa B, Balassi M, Englert P, Kiss A (2015) Betweenness versus linerank. Comput Sci Inf Syst 12(1):33–48. doi:10.2298/CSIS141101092K

    Article  Google Scholar 

  17. Louridas P, Spinellis D, Vlachos V (2008) Power laws in software. ACM Trans Softw Eng Methodol 18(1):2:1–2:26

    Article  Google Scholar 

  18. Mann HB, Whitney DR (1947) On a test of whether one of two random variables is stochastically larger than the other. Ann Math Stat 18(1):50–60. doi:10.2307/2236101

    Article  MathSciNet  MATH  Google Scholar 

  19. McCabe TJ (1976) A complexity measure. IEEE Trans Softw Eng 2(4):308–320. doi:10.1109/TSE.1976.233837

    Article  MathSciNet  MATH  Google Scholar 

  20. Myers CR (2003) Software systems as complex networks: structure, function, and evolvability of software collaboration graphs. Phys Rev E 68(4):046,116. doi:10.1103/PhysRevE.68.046116

    Article  Google Scholar 

  21. Newman MEJ (2003) The structure and function of complex networks. SIAM Rev 45:167–256. doi:10.1137/S003614450342480

    Article  MathSciNet  MATH  Google Scholar 

  22. Rakić G, Budimac Z (2011) Introducing enriched concrete syntax trees. In: Proceedings of the 14th international multiconference on information society (IS), collaboration, software and services in information society (CSS), pp 211–214

  23. Redner S (2005) Citation statistics from 110 years of Physical Review. Phys Today 58(6):49–54. doi:10.1063/1.1996475

    Article  Google Scholar 

  24. Savić M, Rakić G, Budimac Z, Ivanović M (2014) A language-independent approach to the extraction of dependencies between source code entities. Inf Softw Technol 56(10):1268–1288. doi:10.1016/j.infsof.2014.04.011

    Article  Google Scholar 

  25. Šubelj L, Bajec M (2012) Software systems through complex networks science: review, analysis and applications. In: Proceedings of the first international workshop on software mining, SoftwareMining ’12, pp 9–16. ACM, New York, NY, USA. doi:10.1145/2384416.2384418

  26. Taube-Schock C, Walker R, Witten I (2011) Can we avoid high coupling? In: Mezini M (ed) ECOOP 2011 object-oriented programming. Lecture notes in computer science, vol 6813. Springer, Berlin, pp 204–228. doi:10.1007/978-3-642-22655-7_10

  27. Valverde S, Cancho RF, Solé RV (2002) Scale-free networks from optimal design. EPL 60(4):512–517. doi:10.1209/epl/i2002-00248-2

    Article  Google Scholar 

  28. Wen H, DSouza RM, Saul ZM, Filkov V (2009) Evolution of Apache open source software. In: Ganguly N, Deutsch A, Mukherjee A (eds) Dynamics on and of complex networks, modeling and simulation in science, engineering and technology. Birkhuser, Boston, pp 199–215. doi:10.1007/978-0-8176-4751-3_12

    Google Scholar 

  29. Wheeldon R, Counsell S (2003) Power law distributions in class relationships. In: Proceedings of the third IEEE international workshop on source code analysis and manipulation, pp 45–54. doi:10.1109/SCAM.2003.1238030

  30. Yourdon E, Constantine LL (1979) Structured design: fundamentals of a discipline of computer program and systems design, 1st edn. Prentice-Hall Inc., Upper Saddle River

    MATH  Google Scholar 

Download references

Acknowledgements

The authors gratefully acknowledge the support of this work by the Serbian Ministry of Education, Science and Technological Development through project Intelligent Techniques and Their Integration into Wide-Spectrum Decision Support, no. OI174023.

Author information

Authors and Affiliations

  1. Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovića 4, 21000, Novi Sad, Serbia

    Miloš Savić, Mirjana Ivanović & Miloš Radovanović

Authors
  1. Miloš Savić
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mirjana Ivanović
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Miloš Radovanović
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Miloš Savić.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Savić, M., Ivanović, M. & Radovanović, M. Analysis of high structural class coupling in object-oriented software systems. Computing 99, 1055–1079 (2017). https://doi.org/10.1007/s00607-017-0549-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00607-017-0549-6

Keywords

Mathematics Subject Classification

Search

Navigation