Semantic search at Github

ML Techniques

Hamel Husain discusses innovative machine learning techniques for processing code, highlighting the use of graph neural networks and embeddings. He explains how code can be represented as a graph, utilizing relationships like control flow and data flow to extract features. This approach, combined with natural language techniques, enhances semantic search capabilities, allowing for more intuitive code retrieval 1. Husain emphasizes the importance of embeddings, which create a mathematical representation of code and docstrings, facilitating the alignment of code with its natural language description 2.

The idea is, can we learn an embedding for both the doc string in the code such that when you have some sentence that corresponds to code that does that thing, then you can learn that embedding will be very similar.

--- Hamel Husain

This innovative approach is part of ongoing research aimed at improving code search and understanding.

   

NL vs Code

Kyle Polich and Hamel Husain explore the parallels and distinctions between natural language and code processing. While both involve text, code's structured nature offers unique challenges and opportunities for machine learning models. Husain notes that despite the noise in code comments, the vast amount of data available allows models to learn patterns effectively 3. He highlights the role of natural language elements within code, such as comments and docstrings, which provide valuable context for understanding code semantics 4.

Code is often sprinkled about with natural language bits inside of it, as I assume most listeners already know.

--- Hamel Husain

This blend of structured and unstructured data is crucial for developing robust code processing models.

   

Attention & Embeddings

Attention mechanisms and embeddings play a pivotal role in developing models for code and natural language. Hamel Husain describes how embeddings are used to map code and natural language into a shared vector space, enabling more effective semantic search 5. By employing separate models for code and docstrings, embeddings capture the semantic meaning of both, facilitating their alignment in a common space 2. This approach allows for intuitive code retrieval based on natural language queries.

We really just arbitrarily picked an embedding size for this common embedding size. So I think I may have picked 500 as the size of the embedding.

--- Hamel Husain

Such techniques are essential for advancing the capabilities of code search and understanding.