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An ANFIS-Based High Precision Error Iterative Analysis Method (HPEIAM) to Improve Existing Software Reliability Growth Models

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  • Gul Jabeen

    (Karakoram International University Gilgit, Pakistan)

Abstract

Software Reliability Growth Models (SRGMs) are statistical interpolations of software failures by mathematical modeling. Up till now,more than 200 SRGMshave beenproposed to estimate failure occurrence. Research continues to develop more accurate, efficient,and robust models. To overcome the shortcomings of SRGMs and adapt to thecurrent software development process characterized by increasingcomplexity, a high-precision error iterative analysis method (HPEIAM) is proposed in this paper. HPEIAM combines the parametric SRGMs (PSRGMs) predicted results with their residual errors, which are considered as another source of information that can be modeled with an adaptive neuro-fuzzy inference system (ANFIS). The predicted errors are used to correct the PSRGMs forecasted results repeatedly with the help of ANFIS, which is considered a powerful model to deal with non-linear data. Theproposed technique combines the advantages of the neural network with a fuzzy inference system andPSRGMs, which helps to overcome the disadvantages of these models. The performanceof the proposedtechnique is compared with six PSRGMs using three sets of real software failure datasets basedon five criteria. Experimental results demonstrate that the HPEIAM can significantly improve the model fitting and predictive performance of every parametric SRGM.

Suggested Citation

  • Gul Jabeen, 2024. "An ANFIS-Based High Precision Error Iterative Analysis Method (HPEIAM) to Improve Existing Software Reliability Growth Models," International Journal of Innovations in Science & Technology, 50sea, vol. 6(4), pages 1878-1896, November.
    • Handle: RePEc:abq:ijist1:v:6:y:2024:i:4:p:1878-1896
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    References listed on IDEAS

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    1. Pham, Hoang, 2003. "Software reliability and cost models: Perspectives, comparison, and practice," European Journal of Operational Research, Elsevier, vol. 149(3), pages 475-489, September.
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