A Novel Grey Prediction Framework for Integrating Fault Detection and Correction in Software Reliability Estimation

During software testing phases, fault detection and correction processes are carried out simultaneously. However, the fault correction process is not considered in the grey software reliability growth model (SRGM). To solve this problem, this paper proposed a new grey SRGM framework named the grey SRGM integrating correction process (ICP) that considers both fault detection and correction processes. In the new model framework, the corrected faults are directly incorporated into the grey SRGM, and a one-step iterative calculation method is used to estimate the model. Numerical experiments involving four specific models tested on two real datasets validate the effectiveness of grey ICP-SRGM compared to the original grey model framework. Furthermore, the predictive performance of the new model framework is compared with various other prediction methods, including the Brown exponential smoothing model, Holt exponential smoothing model, autoregressive integrated moving average model, support vector regression model, and feedforward neural network model. Comparative analysis demonstrates that the new model framework exhibits superior adaptability when dealing with small samples and highlights its potential for practical applications where data availability is limited.