Data Generation for Testing and Grading SQL Queries

TL;DR

This paper extends the XData system to generate datasets that effectively test and grade a broader range of SQL queries and mutations, improving automated grading and testing accuracy.

Contribution

The authors enhance the XData data generation techniques to cover more SQL query types and mutations, and develop a system for automated SQL query grading.

Findings

XData datasets outperform predefined and manual datasets in grading accuracy.

Extended XData handles a wider variety of SQL queries and mutations.

Automated grading reduces manual effort and improves consistency.

Abstract

Correctness of SQL queries is usually tested by executing the queries on one or more datasets. Erroneous queries are often the results of small changes, or mutations of the correct query. A mutation Q' of a query Q is killed by a dataset D if Q(D) $\neq$ Q'(D). Earlier work on the XData system showed how to generate datasets that kill all mutations in a class of mutations that included join type and comparison operation mutations. In this paper, we extend the XData data generation techniques to handle a wider variety of SQL queries and a much larger class of mutations. We have also built a system for grading SQL queries using the datasets generated by XData. We present a study of the effectiveness of the datasets generated by the extended XData approach, using a variety of queries including queries submitted by students as part of a database course. We show that the XData datasets outperform predefined datasets as well as manual grading done earlier by teaching assistants, while also avoiding the drudgery of manual correction. Thus, we believe that our techniques will be of great value to database course instructors and TAs, particularly to those of MOOCs. It will also be valuable to database application developers and testers for testing SQL queries.