Project 141

Project Summary

Research teams across Google and Alphabet are exploring new ways that machine learning can assist software developers and hope to rally more members of the developer community to help explore this area. Python notebooks provide a unique learning opportunity, because unlike a lot of standard source code, notebooks often follow narrative format, with comment cells implemented in markdown that explain a programmer's intentions for corresponding code cells. An understanding of the relationships between code and markdown could lend to fresh improvements across many aspects of AI-assisted development, such as the construction of better data filtering and preprocessing pipelines for model training, or automatic assessments of a notebook's readability.

The goal of this competition is to understand the relationship between code and comments in Python notebooks. The research team for this project is challenged to reconstruct the order of markdown cells in a given notebook based on the order of the code cells, demonstrating comprehension of which natural language references which code. The research team may be composed of individuals with interests or specialization in: computer engineering, software engineering and data science.

Sustainable Development Goals