Adam Tornhill is a programmer who combines degrees in engineering and psychology. He's the founder of CodeScene where he designs tools for software analysis. He's also the author of the best-selling Your Code as a Crime Scene as well as three other technical books. Adam's other interests include modern history, music, and martial arts.
Large Language Models have enabled machines to write code. The resulting movement, AI-assisted coding, promises to improve developer productivity. However, AI-assisted coding is still in its infancy. This implies that we should embrace it with caution, guardrails, and realistic expectations.
In this talk, Adam presents both the short- and long-term implications of using AI assistants to write code. We do so based on extensive CodeScene research analyzing over 100k AI refactorings in real-world codebases. Based on this data, we debunk the productivity claims of today’s AI assistants; it’s easy to mistake code-writing speed for productivity.
We then step out on a new path, showing how the same line of research introduces a revolutionary technology for supporting auto-generated code improvements. Using real-world demos, you will see the power of AI-assisted coding without the risks as we automatically improve existing code. In conclusion, we explore how these novel tools not only address industry challenges such as technical debt but also underscore the growing significance of comprehending code over mere writing in the age of AI. Join in!
Successful software development requires that you keep code and people in balance so that one supports the other. It's a hard challenge since a piece of code doesn't reveal anything about its socio-technical context. Enter behavioral code analysis, an approach which combines code level metrics with data on how teams interact within the code. Armed with these techniques, we look to reduce organizational friction by focusing on a set of common challenges:
- Identify architectural coordination bottlenecks and understand the technical root causes.
- Visualize implicit dependencies between teams, act to decouple teams.
- Discover knowledge risks by measuring the Truck Factor. Learn how to mitigate it.
- Communicate the scaling risks inherent in Brooks's Law by showing data on how it impacts your delivery.
- Go beyond technical impact by knowing how bad code causes unhappiness, low morale, and increased attrition.
All techniques are demonstrated with examples from real-world codebases. If you are a senior developer, software architect, or technical lead, this presentation will change how you view code. So join in to learn why technical decisions are never merely technical, and how you can stay a step ahead by preventing socio-technical smells
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