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.

Dr. Xin Peng is a deputy dean and professor at the College of Computer Science and Artificial Intelligence in Fudan University. He is a distinguished member of the China Computer Federation (CCF), serving as Vice Director of its Software Engineering Technical Committee and Standing Committee Member of its Open-Source Development Committee. Additionally, he holds the role of Vice Chair of the Automotive Basic Software Branch under the Society of Automotive Engineers of China (SAE-China). As an active academic leader, he co-edits the Journal of Software: Evolution and Process and sits on the editorial boards of several prestigious journals, including ACM Transactions on Software Engineering and Methodology, Empirical Software Engineering, Automated Software Engineering, and Chinese Journal of Software. His research focuses on intelligent software development, cloud-native and AI-driven operations and maintenance (AIOps), and foundational software for intelligent connected vehicles. His work has been recognized with multiple international honors, such as the IEEE Transactions on Software Engineering Best Paper Award, ICSM Best Paper Award, ACM SIGSOFT Distinguished Paper Award, and IEEE TCSE Distinguished Paper Award. He has also contributed significantly to academic communities as general chair and program co-chair of the CCF ChinaSoft Conference (2022, 2023) and as a program committee member for top conferences including ICSE, FSE, ASE, ISSTA, ICSME, and SANER.

Dr. Yijian Wu is an associate professor at the College of Computer Science and Artificial Intelligence in Fudan University. He serves as an executive committee member in the Technical Committee of Software Engineering of the China Computer Federation (CCF) and a vice director of the Technical Committee of Software Engineering of the Shanghai Computer Society. His research interests mainly include code clone detection and analysis, software evolution analysis, bug analysis, and software development data analytics for quality and productivity improvement. He has been honored with the CCF Scientific and Technological Award (Second Prize for Technical Invention, 2021), ACM SIGSOFT Distinguished Paper Award (ICPC 2022), and Shanghai Teaching Achievement Award (Second Prize, 2009).

Dr. Bihuan Chen is an associate professor at the College of Computer Science and Artificial Intelligence in Fudan University. His research interests lie in software engineering, focusing on software supply chain and trustworthy AI. He received three ACM SIGSOFT Distinguished Paper Awards at FSE 2016, ASE 2018, and ASE 2022 and two IEEE TCSE Distinguished Paper Awards at ICSME 2020 and SANER 2023.