Justine Gehring
Research Engineer @ Moderne
Justine Gehring is a talented researcher in the field of Machine Learning (ML) for code and Graph Neural Networks (GNNs). Her focus lies in generating code under challenging circumstances, specifically in scenarios such as sparse data where library-specific code is required, as well as managing a substantial amount of code at a time. Justine is a research engineer at Moderne.
Presentations
Generative AI can be a powerful force multiplier for developers, but it also comes with limitations. Developers are expected to co-create with AI, and check the generated output, or risk hallucinations running wild. This can aid development at a local machine, but what happens when you try to apply these tools on a massive scale?
For mass-scale code operations, AI needs to have agency, able to operate with some degree of autonomy. In this session, we’ll cover how you can combine retrieval and tool calling techniques, the richest code data source for Java called the Lossless Semantic Tree (LST), and OpenRewrite rules-based recipes to drive more efficient and accurate AI model output for refactoring and analyzing large codebases.
You’ll learn about how you can use AI embeddings as a powerful tool to visualize, analyze, and even do smarter sampling for your codebase. Plus, we’ll show you how to leverage GenAI to accelerate writing OpenRewrite deterministic recipes.
We’ll take an honest look back and a look ahead on our process, to show you how enterprises can now reliably leverage AI for code modernization at scale.