Human-Centered Framework for Evaluating AI Agents in Software Engineering
Analysis
This paper addresses a critical gap in AI evaluation by shifting the focus from code correctness to collaborative intelligence. It recognizes that current benchmarks are insufficient for evaluating AI agents that act as partners to software engineers. The paper's contributions, including a taxonomy of desirable agent behaviors and the Context-Adaptive Behavior (CAB) Framework, provide a more nuanced and human-centered approach to evaluating AI agent performance in a software engineering context. This is important because it moves the field towards evaluating the effectiveness of AI agents in real-world collaborative scenarios, rather than just their ability to generate correct code.
Key Takeaways
- •Proposes a shift from evaluating code correctness to assessing collaborative intelligence in AI agents.
- •Introduces a taxonomy of desirable agent behaviors for enterprise software engineering.
- •Presents the Context-Adaptive Behavior (CAB) Framework to account for shifting behavioral expectations.
- •Offers a human-centered foundation for designing and evaluating AI agents in software engineering.
“The paper introduces the Context-Adaptive Behavior (CAB) Framework, which reveals how behavioral expectations shift along two empirically-derived axes: the Time Horizon and the Type of Work.”