Modern Software Analysis: Theory, Methods and Techniques offers a systematic exploration of how Big Data Analytics and AI are reshaping software engineering. Bridging theory and practice, the book highlights cutting-edge approaches to software quality governance, bug detection, supply chain risk management, and intelligent development paradigms.The monograph surveys a broad range of modern techniques in software analysis, including static and dynamic program analysis, code differencing and evolutionary analysis, deep learning–based bug analysis, large language model–powered bug analysis, architectural metrics, design smell detection, and microservices anti-pattern analysis. It further examines data-driven frameworks for software quality traceability, clone management, supply chain vulnerability modeling, license compliance, technical debt quantification, and productivity analytics. Finally, it advances intelligent software development through code asset mining, search-driven recommendation, and generative approaches leveraging AI.This work is intended for researchers, as well as industrial practitioners engaged in software quality governance, process optimization, DevOps efficiency, and AI-driven software engineering. With its balance of conceptual depth and practical relevance, it serves as an essential reference for technical decision-makers and academic investigators navigating the era of data-intensive software development.

Modern Software Analysis: Theory, Methods and Techniques offers a systematic exploration of how Big Data Analytics and AI are reshaping software engineering. Bridging theory and practice, the book highlights cutting-edge approaches to software quality governance, bug detection, supply chain risk management, and intelligent development paradigms.The monograph surveys a broad range of modern techniques in software analysis, including static and dynamic program analysis, code differencing and evolutionary analysis, deep learning–based bug analysis, large language model–powered bug analysis, architectural metrics, design smell detection, and microservices anti-pattern analysis. It further examines data-driven frameworks for software quality traceability, clone management, supply chain vulnerability modeling, license compliance, technical debt quantification, and productivity analytics. Finally, it advances intelligent software development through code asset mining, search-driven recommendation, and generative approaches leveraging AI.This work is intended for researchers, as well as industrial practitioners engaged in software quality governance, process optimization, DevOps efficiency, and AI-driven software engineering. With its balance of conceptual depth and practical relevance, it serves as an essential reference for technical decision-makers and academic investigators navigating the era of data-intensive software development.