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

Gul Jabeen ()
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Gul Jabeen: Karakoram International University Gilgit, Pakistan

International Journal of Innovations in Science & Technology, 2024, vol. 6, issue 4, 1878-1896

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.

Keywords: SoftwareReliability; Software Failures; Residual Errors; Artificial-Neuro-Fuzzy-InferenceSystem; Parametric Software Reliability Growth Models; Prediction Accuracy (search for similar items in EconPapers)
Date: 2024
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