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

Sabit Rahim; Gul Jabeen; Gul Sahar; Luo Ping

Authors

Sabit Rahim Department of Computer Sciences, Karakoram International Uniersity Gilgit
Gul Jabeen Departmnt of Computer Sciences, Karakoram International University Gilgit
Gul Sahar Departmnt of Computer Sciences, Karakoram International University Gilgit
Luo Ping Tsinghua University, Beijing, China

Keywords:

Software Reliability, Software Failures, Residual Errors, Artificial-Neuro-Fuzzy- Inference System, Parametric Software Reliability Growth Models, Prediction Accuracy

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 non-linear 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.

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