Research Article Open Access

Development of Software Reliability Growth Models for Industrial Applications Using Fuzzy Logic

Sultan Aljahdali

Abstract

Problem statement: The utilization of Software Reliability Growth Models (SRGM) plays a major role in monitoring progress, accurately predicting the number of faults in the software during both development and testing processes; define the release date of a software product, helps in allocating resources and estimating the cost for software maintenance. This leads to achieving the required reliability level of a software product. Approach: We investigated the use of fuzzy logic on building SRGM to estimate the expected software faults during testing process. Results: The proposed fuzzy model consists of a collection of linear sub-models, based on the Takagi-Sugeno technique and attached efficiently using fuzzy membership functions to represent the expected software faults as a function of historical measured faults. A data set provided by John Musa of bell telephone laboratories (i.e., real time control, military and operating system applications) was used to show the potential of using fuzzy logic in solving the software reliability modeling problem. Conclusion: The developed models provided high performance modeling capabilities.

Journal of Computer Science
Volume 7 No. 10, 2011, 1574-1580

DOI: https://doi.org/10.3844/jcssp.2011.1574.1580

Submitted On: 4 April 2011 Published On: 9 August 2011

How to Cite: Aljahdali, S. (2011). Development of Software Reliability Growth Models for Industrial Applications Using Fuzzy Logic. Journal of Computer Science, 7(10), 1574-1580. https://doi.org/10.3844/jcssp.2011.1574.1580

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Keywords

  • Software Reliability Growth Models (SRGM)
  • Takagi-Sugeno technique
  • Fuzzy Logic (FL)
  • Artificial Neural Net-works (ANN)
  • Genetic Programming (GP)
  • model structure
  • linear regression model
  • NASA space