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For software developers as well as software product users, fault removal efficiency is crucial. Developers can estimate the amount of work required by evaluating the proportion of addressed faults. At the same time, the level of trust in utilizing the software product can be determined by customers, based on this information. The proposed software reliability growth model considers fault removal efficiency relevant to contemporary scenarios characterized by automated testing and debugging tools. A comparison between the proposed model and other existing models is conducted by utilizing two data sets from software testing. Additionally, the best release plans are created by considering warranty costs, risk costs, and error removal costs while still meeting reliability requirements. [ABSTRACT FROM AUTHOR]

The increasing reliance on computer software has raised significant concerns regarding software reliability evaluation. Over the past four decades, various software reliability models have been developed, encompassing both parametric and nonparametric approaches. However, no single model has demonstrated effectiveness in handling all types of datasets. In response to this challenge, the deep neural network (DNN), a powerful deep learning model, has emerged as a promising solution. By leveraging the flexibility and adaptability of artificial neural networks (ANN), the DNN model exhibits remarkable prediction performance by capturing training variables and exploring deeper layers. This study presents an approach that utilizes a DNN model based on an ANN architecture with multiple activation functions. This approach aims to estimate software reliability and predict the frequency of software flaws. Through extensive experimental analysis and validation, the proposed DNN model surpasses the predictive accuracy achieved by existing parametric and nonparametric models. These results highlight the potential of the DNN model in effectively addressing the complexities involved in software reliability evaluation. By combining the power of deep learning with the adaptability of ANN, the proposed model offers a promising and accurate solution for software reliability prediction. [ABSTRACT FROM AUTHOR]

In this paper, a scheme for constructing software reliability growth model based on Non-Homogeneous Poisson Process is proposed. Here, we consider the software reliability growth model that incorporates with imperfect debugging, change point and testing effort. However, most researchers assume a constant detection rate per fault in deriving their software reliability models. They suppose that all faults have equal probability of being detected during the software testing process, and the rate remains constant over the intervals between fault occurrences. In reality, the fault detection rate strongly depends on the skill of test teams, program size, and software testability. Also in most realistic situations, fault repair has associated with a fault re-introduction rate due to imperfect debugging phenomenon. In this case, the fault detection rate and fault introduction rate will be changed during the software development process. Therefore, here we incorporate both generalized logistic testing-effort function, change-point parameter into software reliability modelling. The Least Square Estimation approach is used to estimate the unknown parameters of the new model. So in our new proposed model we collect software testing data from real application and utilize it to illustrate the proposed model. Experimental results show that the proposed framework to incorporate both testing-effort and change-point for Imperfect-Debugging SRGM has a fairly accurate prediction capability. [ABSTRACT FROM AUTHOR]