A Family of Software Reliability Models with Bathtub-Shaped Fault Detection Rate

Researchers have proposed several software reliability growth models, many of which possess complex parametric forms. In practice, software reliability growth models 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 software reliability growth models possessing a bathtub-shaped fault detection rate and derives stable and efficient expectation conditional maximization algorithms to enable the fitting of these models. The stages of the bathtub are interpreted in the context of the software testing process. The illustrations compare multiple bathtub-shaped and reduced model forms, including classical models with respect to predictive and information theoretic measures. The results indicate that software reliability growth models possessing a bathtub-shaped fault detection rate outperformed classical models on both types of measures. The proposed framework and models may therefore be a practical compromise between model complexity and predictive accuracy.

An Assessment of Incorporating Log-Logistic Testing Effort Into Imperfect Debugging Delayed S-Shaped Software Reliability Growth Model

Software reliability growth models (SRGM) are employed to aid us in predicting and estimating reliability in the software development process. Many SRGM proposed in the past claim to be effective over previous models. While some earlier research had raised concern regarding use of delayed S-shaped SRGM, researchers later indicated that the model performs well when appropriate testing-effort function (TEF) is used. This paper proposes and evaluates an approach to incorporate the log-logistic (LL) testing-effort function into delayed S-shaped SRGMs with imperfect debugging based on non-homogeneous Poisson process (NHPP). The model parameters are estimated by weighted least square estimation (WLSE) and maximum likelihood estimation (MLE) methods. The experimental results obtained after applying the model on real data sets and statistical methods for analysis are presented. The results obtained suggest that performance of the proposed model is better than the other existing models. The authors can conclude that the log-logistic TEF is appropriate for incorporating into delayed S-shaped software reliability growth models.

A modified whale optimization algorithm for parameter estimation of software reliability growth models

Software reliability growth models are nonlinear in nature, so it is difficult to estimate the proper parameters. An estimation method based on a modified whale optimization algorithm in which parameters are estimated is discussed in this paper. The whale optimization algorithm is a new swarm intelligence optimization algorithm. This algorithm is not perfect enough. Based on the analysis of whale optimization algorithm, we point out the disadvantages of whale optimization algorithm, and propose a modified whale optimization algorithm algorithm from four aspects: choice regarding the dimension, exploration control, encircling prey modified, and candidate solution selection. The experimental results based on 34 benchmark functions demonstrate that the proposed modified whale optimization algorithm has better accuracy. The modified whale optimization algorithm is used to predict software reliability by predicting the faults during the software testing process using software faults’ historical data. The proposed modified whale optimization algorithm shows significant advantages in handling a variety of modeling problems such as the exponential model, power model, delayed s-shaped model, and modified sigmoid model. Experimental results show that the fitting accuracy of the modified sigmoid model model is minimal on three data sets. The modified whale optimization algorithm with the modified sigmoid model can provide a better estimate of the software faults.