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In order to further improve the fitting and prediction performance of the existing Non-Homogeneous Poisson Process class software reliability growth models, this paper first conducts an in-depth study of imperfect fault exclusion models from the perspective of the total number of faults growth trend. Considering the linear and differential relationships between the total number of faults function and the cumulative detected faults function, this paper proposes two general imperfect fault exclusion framework models, and the expressions of the cumulative detected faults and the total number of faults function in software are derived. Secondly, this paper compares the performance of the two general imperfect debugging models and the six imperfect debugging models proposed in this paper on six real failure datasets for fitting prediction performance. The results of the case validation show that the general imperfect debugging framework model proposed in this paper has excellent fitting and prediction performance on most of the failure datasets, which proves the effectiveness and practicality of the new modeling framework. Again, this paper presents an in-depth analysis of the performance of the model proposed in this paper and other imperfect debugging models on the datasets to suggest the selection of imperfect debugging models for practical applications. Finally, this paper prospects the future research directions of modeling imperfect debugging models. [ABSTRACT FROM AUTHOR]