Developers spend very little time in the editor due to various reasons such as task and tool switching, looking for information on external platforms, and attending meetings. AI tools like copilot and other AI assistants can help minimize context switching and cognitive load, thus increasing productivity. Measuring productivity can involve assessing context switching, cognitive load, and finding metrics from other fields to quantify mental energy saved.
This week's guest is Eirini Kalliamvakou, a staff researcher at GitHub focused on AI and developer experience. Eirini sits at the forefront of research into GitHub Copilot. Abi and Eirini discuss recent research on how AI coding assistance impacts developer productivity. They talk about how leaders should build business cases for AI tools. They also preview what's to come with AI tools and implications for how developer productivity is measured.
Discussion points:
- (1:49) Overview of GitHub’s research on AI
- (2:59) The research study on Copilot
- (4:48) Defining and measuring productivity for this study
- (7:44) Exact measures and factors studied
- (8:16) Key findings from the study
- (9:45) How the study was conducted
- (11:17) Most surprising findings for the researchers
- (14:01) The motivation for conducting a follow-up study
- (15:34) How the follow-up study was conducted
- (18:42) Findings from the follow-up study
- (21:13) Is AI just hype?
- (26:34) How to begin advocating for AI tools
- (34:44) How to translate data into dollars
- (37:06) How to roll out AI tools to an organization
- (38:47) The impact of AI on developer experience
- (43:24) Implications of AI on how we measure productivity
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