Enhancing Software Reliability Growth Modeling: A Comprehensive Analysis of Historical Datasets and Optimal Model Selections

It requires historical datasets from diverse environments to build accurate and robust software reliability growth models. However, it is very difficult to collect such datasets, particularly in the industrial domain, due to confidentiality and security considerations. To solve this issue, we conducted a thorough investigation of historical datasets for software reliability growth modeling. We searched IEEE Xplore, a prominent digital library, using the keyword 'Software Reliability Growth Model' for publications up to 2022 and gathered 127 non-redundant historical datasets. In this paper, we present a comprehensive analysis of these datasets, which helps advance software reliability growth modeling. We applied seven representative software reliability growth models and found that the Generalized Goel model, which has a concave curve, was the most frequently chosen optimal model for Failure-Count type datasets. However, It is notable that S-shaped curve models remained preferred for the majority of datasets. Categorizing datasets by collection phases, application types, source types, and data flow trends resulted in varied optimal model selections across classifications. These insights contribute to the advancement of software reliability growth modeling, fostering informed decision-making in software development and maintenance.