Dynamic test input generation for database applications
Proceedings of the 2007 international symposium on Software testing and analysis
Generating example data for dataflow programs
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
HAMPI: a solver for string constraints
Proceedings of the eighteenth international symposium on Software testing and analysis
CAV'07 Proceedings of the 19th international conference on Computer aided verification
Constraint-based test database generation for SQL queries
Proceedings of the 5th Workshop on Automation of Software Test
Rex: Symbolic Regular Expression Explorer
ICST '10 Proceedings of the 2010 Third International Conference on Software Testing, Verification and Validation
Full predicate coverage for testing SQL database queries
Software Testing, Verification & Reliability
Qex: symbolic SQL query explorer
LPAR'10 Proceedings of the 16th international conference on Logic for programming, artificial intelligence, and reasoning
Generating test data for killing SQL mutants: A constraint-based approach
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
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SQL queries are usually tested for correctness by executing them on one or more datasets, to see if they give the desired results on each dataset. Erroneous queries are often the result of small changes, or mutations, of the correct query. 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. However, the system could not handle a number of commonly used SQL features. In this paper we extend the XData data generation techniques to handle features such as null values, string constraints, aggregation with constraints on aggregation results, and a class of subqueries, amongst others. We present a study of the effectiveness of our data generation approach for correcting student SQL assignments that were part of a database course. The datasets generated by XData outperform publicly available datasets, as well as manual grading done earlier by teaching assistants.