Mining Software Repositories (MSR) has fueled decades of discovery, transforming how we understand and engineer software. In this talk, I reflect on a journey of charting new “gold mines” that have expanded the world of MSR. I revisit early pioneering efforts in MSR for Trustworthy AI (MSR4TAI), where bug repositories were mined to uncover insights into the reliability of machine learning systems, and in MSR for Automated Program Repair (MSR4APR), where bug fixes from thousands of version control systems were mined to power automated program repair — charting new directions in mining for insight and mining for automation. Building on these foundations, recent work continues this dual pursuit: moving from mining bug repositories to mining LLM ecosystems, uncovering hidden structures and relationships among LLMs to gain deeper insights; and moving from passively mining fix patterns to actively generating synthetic artifacts that fuel automated repair. These new directions challenge and inspire us to rethink what it means to mine software repositories. As we celebrate the paths forged, we look forward to new frontiers — and invite the next generation of miners to discover the unseen riches that lie ahead.

David Lo is the OUB Chair Professor of Computer Science and the founding Director of the Center for Research in Intelligent Software Engineering (RISE) at Singapore Management University. Championing the field of AI for Software Engineering (AI4SE) since the mid-2000s, he has demonstrated how AI — encompassing data mining, machine learning, information retrieval, natural language processing, and search-based algorithms — can transform software engineering data into actionable insights and automation. Through empirical studies, he has identified practitioners’ pain points, characterized the limitations of AI4SE solutions, and explored practitioners’ acceptance thresholds for AI-powered tools. He regularly contributes to the MSR conference and works on various MSR topics, including how MSR provides insight into the reliability of machine learning (ISSRE’12) and powers automated program repair (SANER’16). His contributions have led to over 20 awards, including two Test-of-Time awards and eleven ACM SIGSOFT/IEEE TCSE Distinguished Paper awards, and his work has garnered over 38,000 citations. An ACM Fellow, IEEE Fellow, ASE Fellow, and National Research Foundation Investigator (Senior Fellow), Lo has also served as the GC of MSR’22 and ASE’16, and as a PC Co-Chair for ASE’20, FSE’24, and ICSE’25. For more information, please visit: http://www.mysmu.edu/faculty/davidlo/