Artificial neural network based software fault detection and correction prediction models considering testing effort

Abstract

Software reliability is an important attribute of software quality. To achieve higher reliability, software development must include a testing phase in which faults can be detected and corrected. The software reliability growth model (SRGM) has evolved from modeling merely the fault detection process (FDP) into incorporating the fault correction process (FCP) as well. However, restricted by mathematical tractability, it is difficult to incorporate into analytical models with more complicated factors, such as the dependency between faults and the influence of staffing levels. This limits the application of analytical models. Therefore, it is promising to adopt data-driven methods such as the artificial neural network (ANN) to model the FDP and the FCP as no specific assumptions are needed. In this study, a stepwise prediction model is proposed to model the FDP and the FCP based on the ANN. Testing effort is considered in our model since it has a great influence on fault detection and correction process. Using real data, the performance of different types of neural networks are compared with the analytical model. The empirical study has confirmed the effectiveness of the proposed models. Further, the optimal policy of the software release time is also presented to illustrate the applications.