Developers often search across GitHub issues, pull requests, release notes, documentation, and source files to understand why an error happened and how it was fixed. Traditional code search can find matching text, but it usually does not connect the full troubleshooting trail: the original error report, related issue discussion, fixing pull request, changed files, release version, and updated documentation.
In this session, the speakers will show how to build a GitHub troubleshooting graph with Neo4j. They will demonstrate how public GitHub repository data can be modeled as a graph of Issues, Pull Requests, Files, Releases, Comments, Docs, Error Signatures, and Fix Paths. The session will show how Neo4j can combine semantic search with Cypher traversal to move from an error message or stack trace to the most likely issue, fix PR, affected files, and supporting evidence.
You will learn how to design the graph schema, ingest public GitHub data, create embeddings for issues and docs, use hybrid retrieval, and write Cypher queries that explain the path from problem to fix. The session will also cover how to evaluate the system using closed issues and linked pull requests as ground truth.
By the end, you will understand how graph-based retrieval can go beyond code search and help developers debug faster, trace fixes, analyze upgrade issues, and build more useful AI coding assistants.
Speaker
Sohail Shaikh
Data Scientist
Sohail Shaikh is a Data Scientist at with 9+ years of overall professional experience across AI, NLP, analytics, marketing, and software development. He holds an MS in Information Systems Management from Carnegie Mellon University, an MBA in Marketing, and a Bachelor’s degree in Computer Engineering. His work focuses on building practical AI systems for conversational intelligence, including LLM workflows, RAG pipelines, semantic search, topic modeling, and knowledge extraction from large-scale unstructured data. He has also received professional recognition, including a Flipkart Innovation Award awarded to the top 1% of employees.
