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HNRQ

Helping Novices Learn and Debug Relational Queries

Live demo for RA
Demo video for SQL
Live demo for SQL

In a world where data analytics skills have become an indispensable part of any education and modern workforce, are you still struggling with your relational queries?

HNRQ is about automatically constructing small, simple database instances (counterexamples) to illustrate why queries return wrong results, allowing users to trace query execution over these instances, and leading users to generalize from specific counterexamples to semantic descriptions of what cause wrong results.

HNRQ is still under construction and we will release the prototype for SQL queries in the future.



Acknowledgement: This work is partially supported by the NSF Award IIS-2008107: "III: Small: Helping Novices Learn and Debug Relational Queries". Jun Yang (PI), Sudeepa Roy (co-PI), and Kristin Stephens-Martinez (co-PI). Duke University. 2020-2023. $499,972.

Key Features

Small Counterexample
Finding a small subinstance from the test database illustrating the error
Trace Query Execution
Tracing intermediate results of (correlated) subqueries in a way consistent with how the query was written

Demo

A video demonstrating our prototype for debugging SQL queries

The Team

HNRQ is being developed by a team of undergraduate and graduate students advised by Prof. Jun Yang, Prof. Sudeepa Roy, and Prof. Kristin Stephens-Martinez.

Postdoc

Amir Gilad

Graduate Students

Zhengjie Miao, Yihao Hu
Alumni: Tiangang Chen, Qiulin Li

Undergraduate Students

Zhiming Leong, Haechan Lim, Zachary Zheng, Aanya Sanghavi
Alumni: Alexander Bendeck, Kevin Day, Jeffrey Luo, Aparimeya Taneja, Jeremy Cohen

Publications

  1. Amir Gilad*, Zhengjie Miao*, Sudeepa Roy, Jun Yang. “Understanding Queries by Conditional Instances”. To appear in ACM SIGMOD International Conference on Management of Data (SIGMOD) 2022. Arxiv (* = equal contributions)
  2. Yihao Hu, Zhengjie Miao, Zhiming Leong, Haechan Lim, Zachary Zheng, Sudeepa Roy, Kristin Stephens-Martinez, Jun Yang. “I-Rex: An Interactive Relational Query Debugger for SQL”. In ACM Technical Symposium on Computer Science Education (SIGCSE) 2022, Demonstration Track. Abstract
  3. Zhengjie Miao, Tiangang Chen, Alexander Bendeck, Kevin Day, Sudeepa Roy, Jun Yang. “I-Rex: An Interactive Relational Query Explainer for SQL”. In Proceedings of the VLDB Endowment (PVLDB) 2020, Vol 13, Demonstration Track. Paper
  4. Zhengjie Miao, Sudeepa Roy, Jun Yang. “Explaining Wrong Queries Using Small Examples.” In ACM SIGMOD International Conference on Management of Data (SIGMOD) 2019. Paper
  5. Zhengjie Miao, Sudeepa Roy, Jun Yang. “RATest: Explaining Wrong Relational Queries Using Small Examples”. In ACM SIGMOD International Conference on Management of Data (SIGMOD) 2019, Demonstration track. Paper