IJSRSET calls volunteers interested to contribute towards the scientific development in the field of Science, Engineering and Technology

Home > IJSRSET173117                                                     


Analytical Model for Software Reliability Prediction

Authors(1):

Rasha Gaffer M. Helali
  • Abstract
  • Authors
  • Keywords
  • References
  • Details
The big evolution in software field lead to increase the need for existence of high quality quantitative measurements for both syntactic and semantic features. Considering software products in particular, we found that the most existing tools measure syntactical features only syntactic metrics- that reflect how programs represented in source code, but not what functions that programs define. In this paper, we discuss semantic metrics, which characterize the sets and functions that the programs define; now it would be a useful complement to the vast body of software metrics in use. The results of this study show how semantic metrics can be used as indicator to some factors that affect software reliability.

Rasha Gaffer M. Helali

Semantic Metrics, Software Metrics, Software Quality, Syntactic Metrics

  1. Mili, A. Jaoua, M. Frias, Rasha Gaffer M. Helali. Semantic Metrics for Software Products. Innovations in Systems and Software EngineeringA NASA Journal ISSN 1614-5046r, April 2014.
  2. Afzal, "Metrics in software test planning and test design process,” Blekinge Institute of Technology, 2007
  3. Rau, Steinbeis Transferzentrum Softwaretechnik,. A Whitepaper on Metrics. 1998, 1999, 2001. avilable online at: http://www.it.fhtesslingen.de/~rau/forschung/metrics.htm.
  4. Mills, E. verald E. Software Metrics, SEI Curriculum Module SEI-CM-12-1.1, Carnegie Mellon University, A good overview of product and process metrics with an exhaustive bibliography 1988.
  5. Joe Schofield, The Statistically Unreliable Nature of Lines of Code, CrossTalk, 18(4):29-33, April 2005. Available at http://www.crosstalkonline.org/storage/issue-archives/2005/200504/200504-Schofield.pdf
  6. Norman E.Fenton, Shari Lawrence Pfleeger, Softwai Metrics A Rigorous & Practical Approach, SECOND EDITION, PWS PUBLISHING COMPANY , 20 Park Plaza, Boston, MA 021 16-4324.
  7. Gall, C. S. Inf. Technol. & Syst. Center, Univ. of Alabama in Huntsville, Huntsville, AL Lukins, Stacy K.; Etzkorn, Letha H.; Gholston, Sampson; Farrington, Phillip A.; Utley, Dawn R.; Fortune, J.; Virani, Shamsnaz, Semantic software metrics computed from natural language design specification. Volume: 2 , Issue: 1 Page(s): 17 – 26.2008.
  8. Etzkorn, Letha H. "Semantic metrics, conceptual metrics, and ontology metrics: an analysis of software quality using IR-based systems, potential applications and collaborations." Proc. Int. Conf. Software Maintenance. 2006.
  9. Bo Hu, Yannis Kalfoglou, Harith Alani, David Dupplaw, Paul Lewis, Nigel Shadbolt Semantic metrics, Managing Knowledge in a World of NetworksLecture Notes in Computer ScienceVolume 4248, 2006, pp 166-181.
  10. Fazlollah M. Reza (1961, 1994). An Introduction to Information Theory. Dover Publications, Inc., New York. ISBN 0-486-68210-2.
  11. I Csiszar and J Koerner. Information Theory: Coding Theorems for Discrete Memoryless Systems. CambridgeUniversity Press, 2011.
  12. Shannon, C.E. (1948), "A Mathematical Theory of Communication", Bell System Technical Journal, 27, pp. 379–423 & 623–656, July & October, 1948
  13. Panchenko, Oleksandr, Stephan H. Mueller, and Alexander Zeier. "Measuring the quality of interfaces using source code entropy." Industrial Engineering and Engineering Management, 2009. IE&EM'09. 16th International Conference on. IEEE, 2009.
  14. B. Allen, "Measuring graph abstractions of software: An information-theory approach,”in Proceedings of IEEE Symposium on Software Metrics, 2002, pp. 182- 193.
  15. Yi T. and Wu F., Empirical Analysis of Entropy Distance Metric for UML ClassDiagrams, ACM SIGSOFT Software Engineering Notes, 2004.
  16. Goise, Francois & Olla, Stefano (2008). Entropy methods for the Boltzmann equation: lectures from a special semester at the Centre Émile Borel, Institut H. Poincaré, Paris, 2001. Springer. p. 14. ISBN 978-3-540-73704-9
  17. Amrit Dhillon, Amrinder Singh, Analysis of Software Metrics for Bubble Sort and Selection Sort. International Journal of Computer Applications & Information Technology Vol. 1, No.1, July 2012.
  18. Subject Infrastructure Repository, online at: http://sir.unl.edu/portal/index.php.
  19. Regresstion analysis , avilable online at: https://onlinecourses.science.psu.edu/stat501/node/165.
  20. Nagappan, Nachiappan, and Thomas Ball. "Use of relative code churn measures to predict system defect density." Software Engineering, 2005. ICSE 2005. Proceedings. 27th International Conference on. IEEE, 2005.

Publication Details

Published in : Volume 3 | Issue 1 | January-February - 2017
Date of Publication Print ISSN Online ISSN
2017-02-28 2395-1990 2394-4099
Page(s) Manuscript Number   Publisher
412-420 IJSRSET173117   Technoscience Academy

Cite This Article

Rasha Gaffer M. Helali, "Analytical Model for Software Reliability Prediction ", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 3, Issue 1, pp.412-420, January-February-2017.
URL : http://ijsrset.com/IJSRSET173117.php

IJSRSET Xplore

Subscribe

Conferences

National Conference on Advances in Mechanical Engineering 2017(NCAME 2017)

National Conference on Emerging Trends in Civil Engineering 2017( NCETCE 2017)