CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Analysis of Cognitive Complexity with Cyclomatic Complexity Metric of Software

by Dinuka R. Wijendra, K.P. Hewagamage
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 174 - Number 19
Year of Publication: 2021
Authors: Dinuka R. Wijendra, K.P. Hewagamage
10.5120/ijca2021921066

Dinuka R. Wijendra, K.P. Hewagamage . Analysis of Cognitive Complexity with Cyclomatic Complexity Metric of Software. International Journal of Computer Applications. 174, 19 ( Feb 2021), 14-19. DOI=10.5120/ijca2021921066

@article{ 10.5120/ijca2021921066,
author = { Dinuka R. Wijendra, K.P. Hewagamage },
title = { Analysis of Cognitive Complexity with Cyclomatic Complexity Metric of Software },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2021 },
volume = { 174 },
number = { 19 },
month = { Feb },
year = { 2021 },
issn = { 0975-8887 },
pages = { 14-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume174/number19/31784-2021921066/ },
doi = { 10.5120/ijca2021921066 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:22:32.910537+05:30
%A Dinuka R. Wijendra
%A K.P. Hewagamage
%T Analysis of Cognitive Complexity with Cyclomatic Complexity Metric of Software
%J International Journal of Computer Applications
%@ 0975-8887
%V 174
%N 19
%P 14-19
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The complexity of a software can be derived by using software complexity metrics which determines various software attributes quantitatively. The cognitive complexity metric, which is considering as a prominent factor of calculating the complexity of a software, evaluates how the human brain processes the given software with respective to different aspects, which involves the concept of cognitive Informatics. The McCabe’s cyclomatic complexity is currently using as a standard complexity metric to determine the software complexity in terms of the number of linear independent paths. Thus, a broad analysis is carried on how the cognitive complexity derived based on Cognitive Information Complexity Measure (CICM) and the McCabe’s cyclomatic complexity relates and varies with the computation of the given software, resulting that the cognitive complexity value becomes high with respective to its cyclomatic complexity. The cognitive complexity computation beyond the CICM value does not have a strong linear relation of the computation with cyclomatic complexity, which may be derived with a certain combination of relationships based on the factors involved within the cognitive complexity determination.

References
  1. IEEE Computer Society: IEEE Standard Glossary of Software Engineering Terminology, IEEE Standard 610.12-1990.
  2. J. C. Munsona and T. M. Khoshgoftaar, “The dimensionality of program complexity,” Proceedings of the 11th International Conference on Software Engineering, pp. 245–253, 1989.
  3. D. S. Kushwaha and A. K. Misra, “A Modified Cognitive Information Complexity of Software”, ACM SIGSOFT Software Engineering Notes, vol. 31, no. 1, January 2006.
  4. S. Misra, “A Complexity Measure Based on Cognitive Weights”, International Journal of Theoretical and Applied Computer Sciences, vol. 1, pp. 1-10, 2006.
  5. Y. Wang and J. Shao, “Measurement Of The Cognitive Functional Complexity of Software”, IEEE International Conference on Cognitive Informatics, 2003.
  6. Y. Wang, “The Real-Time Process Algebra (RTPA)”, Annals of Software Engineering: An International Journal, Vol. 14, USA, pp. 235 – 274, 2002.
  7. D. S. Kushwaha and A. K. Misra, “Improved Cognitive complexity Measure: A Metric that Establishes Program Comprehension Effort”, ACM SIGSOFT Software Engineering Notes, vol. 31, no. 5, September 2006
  8. J. K. Chhabra, “Code Cognitive complexity: A New Measure”, World Congress on Engineering, vol. 2, July 6-8 2011.
  9. C. R. Douce, P. J. Layzell and J. Buckley, “Spatial measures of software complexity”, in Proceedings of 11th meeting of Psychology of Programming Interset Group, Leeds, January 1999.
  10. Y. Choe, C. Jong and S. Han, “Software Cognitive Information Measure based on Relation between Structures”, 2013.
  11. T. Klemola and J. Rilling, “A Cognitnve Complexity metric based on Category learning”, in Prceeding of the 2nd IEEE International Conference of Cognitnve Informatics (ICCI’03), 2003.
  12. U. Chhillar and S. Bhasin, “A New Weighted Composite Complexity Measure for Object-Oriented Systems”. International Journal of Information and Communication Technology Research.vol.1, no. 3, July 2011.
  13. D. S. Kushwaha and A. K. Misra, “Cognitive Information Complexity measure of Object Oriented Software – A Practitioner’s Approach”, in Proceedings of the 5th WSEAS International Conference on Software Engineering, Parallel and Distributed Systems, pp174-179, February 15-17, 2006.
  14. E. Weyuker, “Evaluating Software Complexity Measure”, IEEE Transaction on Software Complexity Measure, 14 (9), pp. 1357 – 1365, 1988.
  15. L. Briand and S. Morasca, “Property Based Software Engineering Measurement”, IEEE Transactions on Software Engineering, vol. 22, no. 1, January 1996.
  16. D. S. Kushwaha and A. K. Misra, “ Robustness Analysis of Cognitive Information Complexity Measure using Weyuker Properties” , ACM SIGSOFT Software Engineering Notes, vol. 31, no. 1, January 2006.
  17. S. Misra and A. Misra, “Evaluation and Comparison of Cognitive complexity Measure”, ACM SIGSOFT Software Engineering Notes, vol. 32, no. 2, March 2007.
  18. S. Misra, “Validating Modified Cognitive complexity Measure”, ACM SIGSOFT Software Engineering Notes, vol. 32, no. 3, May 2007.
  19. J. K. Chhabra and V. Gupta, “Evaluation of Object-Oriented Spatial Complexity Measures”, ACM SIGSOFT Software Engineering Notes, vol. 34, no. 3, May 2009.
  20. S. Misra and A. Misra, “Evaluating Cognitive complexity Measure with Weyuker Properties”, Third IEEE International Conference on Cognitive Informatics (ICCI’04), 2004.
  21. T. McCabe, “A Complexity Measure”, IEEE Transactions on Software Engineering, December 1976.
  22. T. McCabe, “Structured Testing: A Testing Methodology Using the Cyclomatic complexity Metric,” NIST Special Publication, pp. 500-235, September 1996.
  23. A. Madi, O. K. Zein and S. Kadry, “ On the Improvement of Cyclomatic complexity Metric”, International Journal of Software Engineering and its Applications, vol 7, No 2, March 2013.
  24. G. K. Gill and C. F. Kemerer, “Cyclomatic complexity Density and Software Maintainance Productivity”, IEEE Transactions of Software Engineering, vol 17, No 2, December 1991.
  25. J. Graylin, R. K. Smith, N. A. Kraft, J. E. Hale and D. Hale, “Cyclomatic complexity and Lines of Code: Empirical Evidence of a Stable Linear Relationship”, International journal of Software Engineering and Applications, pp. 2: 137-143, October 2009.
  26. E. Balagurusamy,Programing in ANSI - C, Tata McGraw-Hill Publishing Company Limited, New Delhi,Second edition, 1992.
Index Terms

Computer Science
Information Sciences

Keywords

BCS CC CICM LOC

Powered by PhDFocusTM