NBSearch: Semantic Search and Visual Exploration of Computational Notebooks

TL;DR

NBSearch is a system that enables semantic search and visual exploration of code within computational notebooks, improving developers' ability to find and understand relevant code snippets efficiently.

Contribution

It introduces a novel system combining machine learning-based semantic search with interactive visualizations tailored for notebook collections.

Findings

Effective semantic search in large notebook collections.

Interactive visualizations reveal intra- and inter-notebook relationships.

System supports natural language queries for code search.

Abstract

Code search is an important and frequent activity for developers using computational notebooks (e.g., Jupyter). The flexibility of notebooks brings challenges for effective code search, where classic search interfaces for traditional software code may be limited. In this paper, we propose, NBSearch, a novel system that supports semantic code search in notebook collections and interactive visual exploration of search results. NBSearch leverages advanced machine learning models to enable natural language search queries and intuitive visualizations to present complicated intra- and inter-notebook relationships in the returned results. We developed NBSearch through an iterative participatory design process with two experts from a large software company. We evaluated the models with a series of experiments and the whole system with a controlled user study. The results indicate the feasibility of our analytical pipeline and the effectiveness of NBSearch to support code search in large notebook collections.