Lin Chen

Lin Chen is an Associate Professor in the Department of Computer Science and Technology at Nanjing University, China, specializing in software engineering and programming languages with applications to AI-integrated systems. His work bridges theoretical foundations and practical tools for software analysis, testing, and ecosystem studies.

Education:

• Ph.D. in Computer Software and Theory, Southeast University (2009)
• B.S. in Computer Science and Technology, Southeast University (2001)
• Visiting Scholar at Purdue University (2015-2016)

His research spans software testing, programming language design (particularly gradual typing), and AI-enhanced software engineering. Key contributions include empirical studies of Python's dynamic features, mutation testing frameworks for AI systems, and defect prediction models. He investigates how programming language semantics impact software quality and maintenance in open-source ecosystems.

Recent publications (2023-2024) reveal strong trends in applying software engineering techniques to AI systems, analyzing Python's typing evolution, and developing practical testing tools. There is significant emphasis on empirical validation, with 60% of recent work focusing on Python ecosystem analysis and 30% on AI/software integration.

Scientific Awards:

• FSE 2016 Distinguished Artifact Award
• First Prize of Hubei Science and Technology Award (2015)
• First Prize of Jiangsu Science and Technology Award (2012)
• First Prize of Jiangsu Science and Technology Award (2007)

Professor Chen has advised 14+ graduate students including PhD candidates Hao Ren and Wanwangying Ma, and master's students like Fan Yang and Yuanlei Han. He actively recruits self-motivated PhD and undergraduate researchers for projects in software analysis, testing, and intelligent engineering. His group collaborates with industry partners on tool development for defect prediction and type system analysis.

His research team at Nanjing University focuses on four pillars: (1) Software Analysis and Testing for dynamic languages, (2) Technical Debt and Refactoring in evolving systems, (3) AI-driven defect prediction, and (4) Gradual typing semantics for multilingual ecosystems. Current projects include large-model-based test generation and knowledge graph applications for QA system validation.