Because of the growing reliance on software, concerns are growing as to how reliable a system is before it is commissioned for use, how high the level of reliability is in the system, and how many vulnerabilities exist in the system before its operationalization. Equally pressing issues include how to secure the system from internal and external security threats that may exist in the face of resident vulnerabilities. These two problems are considered increasingly important because they necessitate the development of tools and techniques capable of analyzing dependability and security aspects of a system. These concerns become more pronounced in the cases of safety-critical and mission-critical systems. System Reliability and Security: Techniques and Methodologies focuses on the use of soft computing techniques and analytical techniques in the modeling and analysis of dependable and secure systems. Software Testing and Quality Assurance: In general, testing is intended to uncover the errors that creep in unintentionally during the design and construction of software systems models or to verify whether the model meets the expected objectives. In contrast to traditional software development, AI ML models are constructed inductively. That is, the behavior of the model is generated designed from training data. Therefore, besides noisy data, the potential causes for undesired behavior in AI ML include execution environment, design mis-specification, poor choice of model, program code, numerical instability, and non-convex objectives.

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