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Dynamic invariant detection for relational databases

database testing
empirical study
invariant detection
Proceedings of the 9th International Workshop on Dynamic Analysis
Authors

Jake Cobb

James A. Jones

Gregory M. Kapfhammer

Mary Jean Harrold

Published

2011

Abstract
Despite the many automated techniques that benefit from dynamic invariant detection, to date, none are able to capture and detect dynamic invariants at the interface of a program and its databases. This paper presents a dynamic invariant detection method for relational databases and for programs that use relational databases and an implementation of the approach that leverages the Daikon dynamic-invariant engine. The method defines a mapping between relational database elements and Daikon’s notion of program points and variable observations, thus enabling row-level and column-level invariant detection. The paper also presents the results of two empirical evaluations on four fixed data sets and three subject programs. The first study shows that dynamically detecting and inferring invariants in a relational database is feasible and 55% of the invariants produced for each subject are meaningful. The second study reveals that all of these meaningful invariants are schema-enforceable using standards-compliant databases and many can be checked by databases with only limited schema constructs.
Details

Paper
Presentation
Presentation

Reference
@inproceedings{Cobb2011,
 author = {Jake Cobb and James A. Jones and Gregory M. Kapfhammer and Mary Jean
Harrold},
 booktitle = {Proceedings of the 9th International Workshop on Dynamic Analysis
},
 title = {Dynamic invariant detection for relational databases},
 year = {2011}
}

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