Software Reliability Prediction Using Hybrid Jaya Optimization and Machine Learning Models (Paperback or Softback)

Software reliability is an important aspect of software engineering because the ability of a software system to operate without failure influences quality, maintainability, availability, and user confidence. Software Reliability Prediction Using Hybrid Jaya Optimization and Machine Learning Models provides a focused technical examination of software reliability prediction, machine learning, optimization, reliability modeling, and the application of the Jaya optimization algorithm to software engineering problems.

The book introduces the fundamental concepts of software reliability and explains the importance of predicting software failures during the development and testing lifecycle. Reliability prediction can help software engineers understand failure behavior, estimate future reliability, identify potential weaknesses, and support decisions related to testing and quality assurance. Traditional software reliability models provide useful mathematical frameworks, but their effectiveness may depend on assumptions about failure processes and available project data.

A central focus is placed on machine learning-based software reliability prediction. Machine learning methods can identify patterns within historical software failure and testing data and use these patterns to estimate future reliability behavior. The book discusses general concepts associated with data preparation, feature selection, model development, training, prediction, validation, and performance evaluation.

The Jaya optimization algorithm is examined as an optimization technique for improving predictive modeling. Optimization can be used to identify suitable parameter combinations, select relevant variables, or improve the performance of machine learning models. The book explores the principles of Jaya optimization and its potential integration with machine learning approaches for software reliability prediction.

The hybrid methodology forms an important part of the book. Combining optimization with machine learning can provide a systematic framework for addressing challenges associated with model parameters, feature selection, and predictive performance. The book considers how an optimization layer can support the development of more effective reliability prediction models while recognizing the importance of appropriate datasets, model validation, and evaluation criteria.

Software reliability data and failure behavior are also examined from a predictive modeling perspective. Software testing can generate observations related to failures, execution time, fault occurrence, and other reliability indicators. Preparing such data appropriately is essential for developing predictive models capable of capturing meaningful relationships between input variables and reliability outcomes.