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DOMINO: Fast and Effective Test Data Generation for Relational Database Schemas | IEEE Conference Publication | IEEE Xplore

DOMINO: Fast and Effective Test Data Generation for Relational Database Schemas


Abstract:

An organization's databases are often one of its most valuable assets. Data engineers commonly use a relational database because its schema ensures the validity and consi...Show More

Abstract:

An organization's databases are often one of its most valuable assets. Data engineers commonly use a relational database because its schema ensures the validity and consistency of the stored data through the specification and enforcement of integrity constraints. To ensure their correct specification, industry advice recommends the testing of the integrity constraints in a relational schema. Since manual schema testing is labor-intensive and error-prone, this paper presents DOMINO, a new automated technique that generates test data according to a coverage criterion for integrity constraint testing. In contrast to more generalized search-based approaches, which represent the current state of the art for this task, DOMINO uses tailored, domain-specific operators to efficiently generate test data for relational database schemas. In an empirical study incorporating 34 relational database schemas hosted by three different database management systems, the results show that DOMINO can not only generate test suites faster than the state-of-the-art search-based method but that its test suites can also detect more schema faults.
Date of Conference: 09-13 April 2018
Date Added to IEEE Xplore: 28 May 2018
ISBN Information:
Conference Location: Västerås, Sweden

I. Introduction

A relational database is often one of an organization's most valuable assets [1]. The integrity constraints specified as part of a schema prevent the insertion of invalid data into a relational database. For instance, “PRIMARY KEY” and “UNIQUE” constraints ensure that data values are distinct, while arbitrary “CHECK” constraints can impose restrictions on data values by, as an example, requiring them to be in a specific range.

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References

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