Bayesian Decision Making of an Imperfect Debugging Software Reliability Growth Model with Consideration of Debuggers’ Learning and Negligence Factors

In this study, an imperfect debugging software reliability growth model (SRGM) with Bayesian analysis was proposed to determine optimal release in order minimize testing costs and also enhance the practicability. Generally, it is not easy estimate parameters by applying MLE (maximum likelihood estimation) or LSE (least squares insufficient historical data. Therefore, situation of data, method can adopt domain experts’ prior judgments utilize few data forecast cost proceed posterior analysis. Moreover, efficiency involves staff’s learning negligent factors, therefore, human factors nature process are taken into consideration developing fundamental model. Based on this, estimation model’s would be more intuitive easily evaluated experts, which major advantage for extending related applications practice. Finally, numerical examples sensitivity analyses performed provide managerial insights useful directions strategies.