Abstract
Fault correction is costly and time-consuming in the software development process [1-5]. Thus the measurement and prediction of the fault correction process is important for the software project manager. In the past, researchers used software reliability growth models to describe the fault correction process. They assumed that fault detection is a non-homogeneous Poisson process, and many studies have ignored the time lag between fault detection and fault correction to simplify the models. Recently, the queueing theory has been introduced to the software debugging process, since many researchers are aware that the fault correction time should not be ignored. The engineers need time to identify the reasons for the failures and remove the root causes. Considering the correction time, several infinite server queueing models are proposed to measure and analyze the software project. A small number of research has even considered the limited number of debuggers and has presented finite server queueing models. However, the previous work focused only on the debugging time lag and the number of servers. There is little research about fault classification. If the faults would be classified, the fault can be assigned to a specific engineer to get better service. The aim of this study is to apply the express queueing theoretic approach for reducing the mean resolution time of faults and to support the management of the software project, such as staffing, cost, utilization, etc. The debugging process is modeled by the queueing theory, and the detected faults will be classified and dispatched into either express queue or regular queue. The experiments show that the performance of the debugging system improved while using the proposed approach. Besides, the simulation procedures we developed not only verify the results of the express queueing model, but also provide a useful tool for project managers to monitor the change of the system in more detail.