This book addresses the stages of fault detection and fault correction in software engineering from a dynamic perspective, that is, the life cycle of the software product, including maintenance phases or an agile methodology.

Software reliability necessitates processes for fault detection and fault correction, but these are not usually considered within standard software life cycles. That is, it is commonly assumed that detecting and/or fixing a software bug is an immediate task, but this is completely unrealistic in many ways: in duration, in the amount of human effort, and also in success rate. For instance, there are usually four types of faults: (1) easy to detect and fix, (2) easy to detect but difficult to fix, (3) difficult to detect but easy to fix, and (4) difficult to detect and fix. Nowadays, many software engineering methodologies incorporate processes for assessing a specific reliability measure, for estimating task costs, and for determining effective release dates for software updates.

This book presents several models for fault detection and fault correction and includes many examples based on realistic datasets, so that the different models can be easily exercised. The book is divided into self-contained chapters that include their own introduction and bibliography. The chapters are sometimes disconnected and include repetitive material, but this is understandable in order to be self-contained. Since this is a “SpringerBriefs” book, each chapter can be seen as a standalone research article. The reader must also be familiar with probability theory; stochastic processes, such as Markov chains; some machine learning notions, such as neural networks; and some modeling and simulation techniques, such as MATLAB Simulink.

In summary, this is an interesting book on fault detection and fault correction processes in software engineering, with good related work and useful datasets for exercising the models; however, there are not many illustrative examples and the different models are not fully explained for nonexperts.