The Evolution of Information Seeking in Software Development: Understanding the Role and Impact of AI Assistants
Ebtesam Al Haque, Chris Brown, Thomas D. LaToza, Brittany Johnson
TL;DR
The paper investigates how AI assistants reshape information seeking in software development and their impact on productivity and skill development. It adopts a convergent mixed-methods approach, combining a survey of 128 valid responses and 17 interviews to capture both breadth and depth of AI tool usage and its effects. The findings show AI tools can increase efficiency in information seeking, but practitioners must manage validation burden, avoid overreliance, and preserve foundational knowledge to sustain long-term expertise. The authors propose tool-level directions, including Retrieval-Augmented Development Environments, adaptive learning, and task-specific fine-tuning, to achieve practical productivity gains while supporting learning and knowledge transfer.
Abstract
About 32% of a software practitioners' day involves seeking and using information to support task completion. Although the information needs of software practitioners have been studied extensively, the impact of AI-assisted tools on their needs and information-seeking behaviors remains largely unexplored. To addresses this gap, we conducted a mixed-method study to understand AI-assisted information seeking behavior of practitioners and its impact on their perceived productivity and skill development. We found that developers are increasingly using AI tools to support their information seeking, citing increased efficiency as a key benefit. Our findings also amplify caveats that come with effectively using AI tools for information seeking, especially for learning and skill development, such as the importance of foundational developer knowledge that can guide and inform the information provided by AI tools. Our efforts have implications for the effective integration of AI tools into developer workflows as information retrieval systems and learning aids.