Software Reliability Models with Bathtub-shaped Fault Detection
Maskura Nafreen, Lance Fiondella · 2021
Researchers have proposed a multitude of software reliability growth models (SRGM), many of which possess complex parametric forms. In practice, SRGM should exhibit a balance between predictive accuracy and other statistical measures of goodness of fit, yet past studies have not always performed such balanced assessment. This paper proposes a framework for SRGM possessing a bathtub-shaped fault detection rate and derives stable and efficient expectation conditional maximization algorithms to fit these models. The illustrations compare multiple bathtub-shaped and classical models with respect to predictive and information theoretic measures. Our results indicate that SRGM possessing a bathtub-shaped fault detection rate outperformed classical models on both types of measures. The proposed framework and models may therefore be a reasonable compromise between model complexity and predictive accuracy.