A modified Yule process to model the evolution of some object-oriented system properties

Information Sciences

Volumn 181, Issue 4, 2011, Pages 883-902

  Turnu, I ^a   Concas, G ^a   Marchesi, M ^a   Pinna, S ^a   Tonelli, R ^a  

^a UNIVERSITY OF CAGLIARI   (Italy)

Author keywords

Java; Object oriented languages; Power laws; Software engineering; Yule process

Indexed keywords

EMPIRICAL DATA; INHERITANCE HIERARCHIES; JAVA; LARGE OBJECTS; MODEL-BASED; NETBEANS; OBJECT-ORIENTED LANGUAGES; OBJECT-ORIENTED SYSTEM; POWER LAW; POWER LAW DISTRIBUTION; SIMULATION APPROACH; SOFTWARE VERSIONS; SYSTEM PROPERTY; YULE PROCESS;

COMPUTER SIMULATION; COMPUTER SOFTWARE; JAVA PROGRAMMING LANGUAGE;

OBJECT ORIENTED PROGRAMMING;

EID: 78649820073     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2010.10.022     Document Type: Article

References (40)

1. Apache Ant. .
2. Cvs. .
3. Eclipse. .
4. NetBeans. .
5. JDK. .
6. Svn. .
7. C. Andersson, and P. Runeson A replicated quantitative analysis of fault distributions in complex software systems IEEE Trans. Softw. Eng. 33 2007 273 286
8. A. Barabasi, and R. Albert Emergence of scaling in random networks Science 286 1999 509 512
9. H. Bauke Parameter estimation for power-law tail distributions by maximum likelihood methods Eur. Phys. J. B 44 2007 167 173
10. G.J. Baxter, M.R. Frean, Software graphs and programmer awareness. , 2008.
11. S.R. Chidamber, and C.F. Kemerer A metric suite for object-oriented design IEEE Trans. Softw. Eng. 20 1994 476 493
12. A. Clauset, C.R. Shalizi, and M.E.J. Newman Power-law distributions in empirical data SIAM Rev. 51 2009 661 703
13. G. Concas, M. Marchesi, S. Pinna, and N. Serra On the suitability of Yule process to stochastically model some properties of object-oriented systems Physica A 370 2006 817 831
14. G. Concas, M. Marchesi, S. Pinna, and N. Serra Power-laws in a large object-oriented software system IEEE Trans. Softw. Eng. 33 2007 687 708
15. A. de Moura, Y. Lai, and A. Motter Signatures of small-world and scale-free properties in large computer programs Phys. Rev. E 68 2003 017102
16. D. de Solla Price A general theory of bibliometric and other cumulative advantage processes J. Am. Soc. Inform. Sci. 27 1976 292 306
17. N. Fenton, and N. Ohlsson Quantitative analysis of faults and failures in a complex software system IEEE Trans. Softw. Eng. 26 2000 797 814
18. S. Focardi, M. Marchesi, and G. Succi A stochastic model of software maintenance and its implications on extreme programming processes Extreme Programming Examined The XP Series 2000 Addison-Wesley pp. 191-206
19. M.L. Goldstein, S.A. Morris, and G.G. Yen Problems with fitting to the power-law distribution Eur. Phys. J. B 41 2004 255 258
20. A. Gorshenev, and Y. Pis'mak Punctuated equilibrium in software evolution Phys. Rev. E 70 2004
21. B.M. Hill A simple general approach to inference about the tail of a distribution Ann. Stat. 3 1975 1163 1174
22. M. Ichii, M. Matsushita, K. Inoue, An exploration of power-law in use-relation of Java software systems, in: 19th Australian Software Engineering Conference, ASWEC 2008, 2008, pp. 422-431.
23. C. Kai-Yuan, and Y. Bei-Bei Software execution processes as an evolving complex network Inf. Sci. 179 2009 1903 1928
24. W. Li The Big Bang Grapha colored graph representation of software design Casey B. Yarnall, ACM Computer Science Research Trends 2008 Nova Science Publishers
25. P. Louridas, D. Spinellis, and V. Vlachos Power laws in software ACM Trans. Softw. Eng. Meth. 18 2008 1 26
26. M. Marchesi, S. Pinna, N. Serra, S. Tuveri, Power laws in smalltalk, in: ESUG-Conference 2004, Research Track, 2004.
27. M. Mitzenmacher A brief history of generative models for power law and lognormal distributions Internet Math. 1 2004 226 251
28. C. Myers Software systems as complex networks: structure, function, and evolvability of software collaboration graphs Phys. Rev. E 68 2003
29. M. Newman Power laws, pareto distributions and zipf's law Contemp. Phys. 46 2005 323 351
30. T.J. Ostrand, E.J. Weyuker, The distribution of faults in a large industrial software system, in: Proceedings of ACM SIGSOFT International Symposium on Software Testing Analysis (ISSTA'02), vol. 27, 2002, pp. 55-65.
31. T.J. Ostrand, E.J. Weyuker, and R.M. Bell Predicting the location and number of faults in large software systems IEEE Trans. Softw. Eng. 31 2005 340 355
32. A. Potanin, J. Noble, M. Frean, and R. Biddle Scale-free geometry in object-oriented programs Commun. ACM 48 2005 99 103
33. H. Seal The maximum likelihood fitting of the discrete pareto law J. Inst. Actuaries 78 1952 115 121
34. H. Simon On a class of skew distribution functions Biometrika 42 1955 425 440
35. T. Tamai, T. Nakatani, Analysis of software evolution processes using statistical distribution models, in: Proceeding of the International Workshop on Principles of Software Evolution, IWPSE'02, Orlando, Florida, 2002, pp. 120-123.
36. S. Valverde, R. Ferrer-Cancho, and R. Solé Scale-free networks from optimal design Europhys. Lett. 60 2002 512 517
37. S. Valverde, R. Solé, Hierarchical small worlds in software architecture, Working Paper 03-07-044, Santa Fe Institute, Santa Fe, NM, 2003.
38. R. Wheeldon, S. Counsell, Power law distributions in class relationships, in: Proceeding on Third IEEE International Workshop on Source Code Analysis and Manipulation (SCAM'03), 2003, pp. 45-57.
39. K. Yamasaki, K. Matia, D. Fu, S.V. Buldyrev, F. Pammolli, M. Riccaboni, H.E. Stanley, Preferential attachment and growth dynamics in complex systems, Phys. Rev. E, 74 (2006).
40. H. Zhang On the distribution of software faults IEEE Trans. Softw. Eng. 34 2008