An ANFIS-Based High Precision Error Iterative Analysis Method (HPEIAM) to Improve Existing Software Reliability Growth Models
Journal Title: International Journal of Innovations in Science and Technology - Year 2024, Vol 6, Issue 4
Abstract
Software Reliability Growth Models (SRGMs) are statistical interpolations of software failures by mathematical modeling. Up till now, more than 200 SRGMs have been proposed to estimate failure occurrence. Research continues to develop more accurate, efficient, and robust models. To overcome the shortcomings of SRGMs and adapt to the current software development process characterized by increasing complexity, 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 nonlinear data. The proposed technique combines the advantages of the neural network with a fuzzy inference system and PSRGMs, which helps to overcome the disadvantages of these models. The performance of the proposed technique is compared with six PSRGMs using three sets of real software failure datasets based on five criteria. Experimental results demonstrate that the HPEIAM can significantly improve the model fitting and predictive performance of every parametric SRGM.
Authors and Affiliations
Gul Jabeen, Sabit Rahim1, Gul Sahar, Luo Ping
Keywords
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