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In the contemporary landscape, software applications play an unparalleled role, infiltrating every facet of our daily lives. This pervasive influence has generated an unprecedented demand for software systems that not only exhibit efficiency but also ensure reliability and resilience. The central objective of this research is to conduct a systematic literature review, scrutinizing its practices, contributions, and potential applications in the evolving realm of software reliability growth modeling. Leveraging robust bibliometric analysis, the study extracts relevant papers from the Scopus database using targeted keywords. The research addresses key questions: (1) identification of pivotal journals, influential authors, and network map in software reliability growth modeling; (2) delineation of theme-based clusters from keyword co-occurrences; and (3) formulation of a research framework for researchers. The study's insights reveal significant publications, temporal patterns, and contemporary relevance in software reliability growth models. By identifying theme-based clusters, the research provides a roadmap for prospective scholars to explore diverse themes and frameworks, contributing to the evolving understanding of software reliability growth models. [ABSTRACT FROM AUTHOR]

Software reliability is a critical aspect of ensuring the quality and dependability of software systems. However, existing software reliability models often make assumptions that do not align with real-world scenarios, such as perfect fault removal and independent faults. In this paper, we address this gap by developing a software reliability model that considers fault-dependent detection, imperfect fault removal, and the maximum number of faults that may present in the system. By accounting for these factors, our proposed model aims to provide a more accurate representation of software reliability. We evaluate the effectiveness of our model by comparing it to existing models using three commonly used goodness-of-fit criteria. The results demonstrate the importance of incorporating these considerations in software reliability modeling and highlight the superiority of our approach in capturing the complexities associated with software faults. Additionally, this paper conducts an analysis of optimal release planning, which yields highly encouraging results for software managers and engineers. This analysis adds significant value to the existing literature, further emphasizing the practical relevance of our proposed model. [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]