Ian Butler

This talk addresses the current limitations of AI coding agents in identifying and fixing bugs, highlighting their tendency to generate false positives and struggle with complex codebases. It proposes practical strategies for developers to improve the effectiveness of these agents, focusing on structured rules, better context management, and leveraging advanced "thinking" models. The core thesis is that while agents show promise, their current implementation requires careful guidance and configuration to be truly useful for developers.

This talk investigates the effectiveness of AI agents in identifying and fixing bugs within software development, moving beyond traditional feature development benchmarks. The presenters introduce a new benchmark designed to evaluate agents on maintenance tasks, highlighting that while agents can often patch simple bugs, their ability to comprehensively detect them, especially complex ones, is still in its early stages. The research suggests current agents struggle with holistic code evaluation and deep reasoning, leading to missed bugs and high false positive rates.