Data-driven software engineering is currently navigating the low point of the “Happiness U-Curve.” Twenty-five years ago, we began at a peak of optimism, where recurring patterns in code promised a future of data-driven software analytics that could reduce the cost of code generation, bug fixes, and code migration. However, the recent AI coding surge has led to a “durability crisis,” landing the field in a low point, where the primary bottleneck is no longer the creation of code, but its validation. While fuzzing with well-formed inputs remains necessary, it lacks actionability for AI-generated systems due to the oracle problem caused by a lack of specifications.

The path to the next peak lies in recognizing that, “if recurring patterns in code got us where we are, recurring patterns in specifications and property-based testing are what will lead us out.” This keynote argues for a fundamental shift from generation to validation. By leveraging property-skeletons that abstract recurring patterns in validation, we can diversify AI-generated specifications, ease the path for proof-engineering, and reduce hallucinations in software testing. The key enabler is a dual-track alignment approach: validating specifications against formal models, while simultaneously ensuring whether such specifications hold true on a real implementation through systematic property-based testing. By bridging the specification world of proof-engineering and the testing world of property-based testing, we can proactively transform AI-driven coding into an AI-assisted, sustainable, and validated discipline.

Miryung Kim is a Professor and Vice Chair of Graduate Studies in UCLA’s Computer Science Department. Recognizing an industry-wide shift toward data-intensive software engineering, she led early research on the role of data scientists in software teams. Her current research focuses on developer tools for data and compute-intensive systems, addressing scale and complexity challenges that traditional debugging and testing cannot meet. Her research established the significance of code clones in software evolution, demonstrating how recurring patterns can automate bug fixes and refactoring—insights that inform today’s AI-driven developer tools. For these contributions to data-driven software analytics, she received the IEEE TCSE New Directions Award. She was honored with the ACM SIGSOFT Influential Educator Award; eight of her former students and postdocs now hold faculty positions at institutions such as Columbia, Purdue, and Virginia Tech, and five of them received NSF CAREER awards. She served as Program Co-Chair of FSE, delivered keynotes at ASE and ISSTA, and is currently an Amazon Scholar at AWS.