Software reliability growth modeling and analysis with dual fault detection and correction processes

Computer software is widely applied in safety-critical systems. The ever-increasing complexity of software systems makes it extremely difficult to ensure software reliability, and this problem has drawn considerable attention from both industry and academia. Most software reliability models are built on a common assumption that the detected faults are immediately corrected; thus, the fault detection and correction processes can be regarded as the same process. In this article, a comprehensive study is conducted to analyze the time dependencies between the fault detection and correction processes. The model parameters are estimated using the Maximum Likelihood Estimation (MLE) method, which is based on an explicit likelihood function combining both the fault detection and correction processes. Numerical case studies are conducted under the proposed modeling framework. The obtained results demonstrate that the proposed MLE method can be applied to more general situations and provide more accurate results. Furthermore, the predictive capability of the MLE method is compared with that of the Least Squares Estimation (LSE) method. The prediction results indicate that the proposed MLE method performs better than the LSE method when the data are not large in size or are collected in the early phase of software testing.