Cognitive learning has been applied in various fields for the purpose of discussing human behavior. In this study, we employ learning functions in software reliability growth models (SRGMs) and consider the conscious/unconscious behavior and multiple learning effects in the models simultaneously to discuss the influence of learning effects on work performance in software debugging projects and also the influence of variations in the environment on learning effects. The models are based on Chiu's models  and employed a sine function  to describe the variable potential errors and to judge change-points in the software debugging process. The results showed almost perfect fitting for the models to the actual data sets, which means the staff engaged in software debugging projects have not only conscious learning effects but also unconscious behavior that can describe variable potential errors and explain the change-points in a software debugging process. This paper also examines the effectiveness of the proposed models and discuss when and what kinds of learning effects occur and how these influence software reliability those help managers to master the software debugging process, the performance of the staff involved in this process, the reliability of the software system, and in the employment of suitable methods to manage organizations.