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The comprehensive review of neurosymbolic programming introduces the reader to the topic and provides an insightful treatise on an increasingly important topic at the intersection of programming languages and machine learning.

The book is a self-contained treatment of a topic that sits at the intersection of machine learning and formal verification. It can serve as an introduction to the field for first-year graduate students or senior undergraduates, even if they have not been exposed to deep learning or verification.

Inspired by the nanopass framework for teaching compilation, this book shows how to implement refinement types via a progression of languages that incrementally add features to the language or type system. The book provides an insightful introduction into Refinement Types using an innovative tutorial style that enables fast learning.

Provides a comprehensive introduction to the field of shape analysis, and presents the foundation of the topic in an accessible manner to readers who are not familiar with it. To do so, the authors characterize the essence of shape analysis compared to more classical pointer analyses.

Examines the progress properties of concurrent objects. The book formulates each progress property in terms of contextual refinement so that, when verifying clients of the objects, concrete object implementations can be replaced with their abstractions with certainty, achieving modular verification.

Covers the timeline and research literature concerning proof development for program verification, including theories, languages, and tools. The book emphasizes challenges and breakthroughs at each stage in history and highlights challenges that are currently present due to the increasing scale of proof developments.