CodeQL for Secure and Efficient Software Analysis (eBook, ePUB)
The Complete Guide for Developers and Engineers
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"CodeQL for Secure and Efficient Software Analysis" "CodeQL for Secure and Efficient Software Analysis" is an authoritative and comprehensive guide for software engineers, security practitioners, and advanced developers seeking to master CodeQL-a groundbreaking static analysis engine that treats code as data. Beginning with a thorough foundation in CodeQL's architecture and its essential place within modern security workflows, the book examines the principles of static code analysis, the nuances of supported programming languages, and the robust database mechanisms that drive scalable, query-b...
"CodeQL for Secure and Efficient Software Analysis" "CodeQL for Secure and Efficient Software Analysis" is an authoritative and comprehensive guide for software engineers, security practitioners, and advanced developers seeking to master CodeQL-a groundbreaking static analysis engine that treats code as data. Beginning with a thorough foundation in CodeQL's architecture and its essential place within modern security workflows, the book examines the principles of static code analysis, the nuances of supported programming languages, and the robust database mechanisms that drive scalable, query-based inspection. Readers are seamlessly introduced to both local and cloud-based environments, enriched by critical comparisons with alternative analysis tools to facilitate informed decision-making. Delving deeper, the book provides a meticulous exploration of QL, CodeQL's powerful query language. From foundational syntax and logical structures to advanced topics such as dataflow analysis, taint tracking, modular query design, and performance optimization, it equips readers with practical skills for constructing high-performance, reusable queries. The chapters address real-world needs, including vulnerability identification and remediation, automated discovery of code quality issues, and continuous integration within secure development pipelines. Case studies and best practices illustrate how CodeQL uncovers significant bugs and inefficiencies, offering actionable insights at every turn. Beyond fundamental techniques, this volume caters to enterprise-scale application-with topics ranging from distributed query execution and business intelligence integration, to governance, compliance, and incident response. It empowers organizations to operationalize CodeQL in diverse, decentralized, and regulated development ecosystems. Capping off with a forward-looking analysis of emerging trends-such as the convergence of machine learning and program analysis, automated query synthesis, and the future landscape of secure software-this book stands as an indispensable resource for anyone committed to advancing software security and quality through programmatic analysis.
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