CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
Reseach Article

Investigating the Strength of Relationships among Software Metrics

by Mandeep K. Chawla, Indu Chhabra
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 81 - Number 10
Year of Publication: 2013
Authors: Mandeep K. Chawla, Indu Chhabra
10.5120/14048-2213

Mandeep K. Chawla, Indu Chhabra . Investigating the Strength of Relationships among Software Metrics. International Journal of Computer Applications. 81, 10 ( November 2013), 21-25. DOI=10.5120/14048-2213

@article{ 10.5120/14048-2213,
author = { Mandeep K. Chawla, Indu Chhabra },
title = { Investigating the Strength of Relationships among Software Metrics },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 81 },
number = { 10 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 21-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume81/number10/14048-2213/ },
doi = { 10.5120/14048-2213 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:56:10.677668+05:30
%A Mandeep K. Chawla
%A Indu Chhabra
%T Investigating the Strength of Relationships among Software Metrics
%J International Journal of Computer Applications
%@ 0975-8887
%V 81
%N 10
%P 21-25
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Software metrics provide desirable means to measure design traits of an application under development as well as quality of end product. These are beneficial at various stages to enhance developer productivity and to make the software more manageable post-deployment. Investigating the strength of relationships among these metrics can offer more meaningful insights than analyzing them in isolation. This paper carries out a case study on an open source java based web server to identify correlations between several metrics from well known OO metrics suites. Quantitative distributions of classes over different metric values have also been observed. Results have been compared with similar past studies to verify the findings.

References
  1. Institute of Electrical and Electronics Engineers. IEEE Standard Glossary of Software Engineering Terminology. IEEE Std 610. 12-1990.
  2. Chidamber S. and Kemerer C. 1994. A Metrics Suite for Object Oriented Design. IEEE Transactions on Software Engineering, vol. 2O, no. 6, pp. 476-493.
  3. Nagappan, N. , Ball, T. , Zeller, A. 2006. Mining metrics to predict component failures. In ICSE, 452-461.
  4. Y. Jiang, B. Cukic, T. Menzies, N. Bartlow 2008. Comparing Design and Code Metrics for Software Quality Prediction. In Proceedings of the Workshop on Predictive Models in Software Engineering (PROMISE'08), Leipzig, Germany.
  5. Yutao Ma, Keqing He, Bing Li, Jing Liu, Xiao-Yan Zhou 2010. A Hybrid Set of Complexity Metrics for Large-Scale Object-Oriented Software Systems. J. Comput. Sci. Technol. 25(6): 1184-1201.
  6. Yasunari Takai, Takashi Kobayashi, Kiyoshi Agusa. 2011. Software Metrics based on Coding Standards Violations. In Proc. the Joint Conference of the 21th International Workshop on Software Measurement and the 6th International Conference on Software Process and Product Measurement (IWSM/MENSURA2011) pp. 273-278, Nara, Japan.
  7. K. A. M. Ferreira, M. A. S. Bigonha, R. S. Bigonha, L. F. O. Mendes, and H. C. Almeida 2012. Identifying thresholds for object oriented software metrics. The Journal of Systems and Software, vol. 85, no. 2, pp. 244–257.
  8. A. Meneely, B. Smith, and L. Williams 2012. Validating software metrics: A spectrum of philosophies. ACM Transactions on Software Engineering and Methodology (TOSEM).
  9. Giulio Concas, Michele Marchesi, Giuseppe Destefanis, Roberto Tonelli 2012. An Empirical Study of Software Metrics for Assessing the Phases of an Agile Project. International Journal of Software Engineering and Knowledge Engineering 22(4): 525-548.
  10. Mel Ó Cinnéide, Laurence Tratt, Mark Harman, Steve Counsell, Iman Hemati Moghadam 2012. Experimental assessment of software metrics using automated refactoring. ESEM, 49-58, ACM.
  11. Raymond P. L. Buse, Thomas Zimmermann 2012. Information needs for software development analytics. . ICSE, 987-996, ACM.
  12. Eric Bouwers, Arie van Deursen, and Joost Visser, 2013. Evaluating Usefulness of Software Metrics – an Industrial Experience Report. In Proceedings International Conference on Software Engineering (ICSE), Software Engineering in Practice (SEIP) track, ACM/IEEE.
  13. D Radjenovic, M Hericko, Richard Torkar, Ales Zivkovic, 2013. Software Fault Prediction Metrics: A Systematic Literature Review. Information & Software Technology 55(8): 1397-1418.
  14. John Michura, Miriam A. M. Capretz, and Shuying Wang, 2013. Extension of Object-Oriented Metrics Suite for Software Maintenance. ISRN Software Engineering,
  15. D. Spinellis 2005. Tool Writing: A Forgotten Art?, IEEE Software, Vol. 22, No. 4, pp 9-11.
  16. Jagdish Bansiya and Carl G. Davis 2002. A hierarchical model for object-oriented design quality assessment. Software Engineering, IEEE Transactions on, 28(1):4–17.
  17. C. Spearman 1987. The proof and measurement of association between two things. The American Journal of Psychology, 100(3/4):441–471.
  18. S. Herbold, J. Grabowski, and S. Waack 2011. Calculation and optimization of thresholds for sets of software metrics", Journal of Empirical Software Engineering, vol. 16, no. 6, pp. 812–841.
  19. L. Rosenberg and L. Hyatt 2001. Software Quality Metrics for Object-Oriented System Environments. NASA Technical Report SATC no. 1, pp 11-58.
  20. Olague, H. , Etzkorn, L. , Gholston, S. , & Quattlebaum, S. 2007. Empirical validation of three software metrics suites to predict fault-proneness of object-oriented classes developed using highly iterative or agile software development processes. IEEE Transactions on Software Engineering, 33(8), pp. 402–419.
  21. Jie Xu, Danny, Ho and Luiz, Fernando Capretz. 2008. An empirical validation of object-oriented design metrics for fault prediction. J. of Comp. Science 4, 7, 571—577.
  22. Jureczko M. 2011. Significance of Different Software Metrics in Defect Prediction. Software Engineering: An International Journal. No 1(1), 86-95.
  23. Okutan, A. , O. T. Yildiz 2012. Software Defect Prediction using Bayesian Networks. Empirical Software Engineering, doi: 10. 1007/s10664-012-9218-8.
  24. M. Badri and F. Toure 2012. Empirical Analysis of Object-Oriented Design Metrics for Predicting Unit Testing Effort of Classes. Journal of Software Engineering and Applications, Vol. 5 No. 7, pp. 513-526.
  25. Eric Bouwers, Arie van Deursen, Joost Visser 2013. Software metrics: pitfalls and best practices. ICSE 1491-1492.
Index Terms

Computer Science
Information Sciences

Keywords

Software Quality CK metrics Apache Tomcat ckjm data distribution correlation.