Automatic Detection and Removal of Ineffective Mutants for the Mutation Analysis of Relational Database Schemas

by Phil McMinn, Chris J. Wright, Colton J. McCurdy, and Gregory M. Kapfhammer

IEEE Transactions on Software Engineering (To Appear), 2018



Data is one of an organization’s most valuable and strategic assets. Testing the relational database schema, which protects the integrity of this data, is of paramount importance. Mutation analysis is a means of estimating the fault-finding “strength” of a test suite. As with program mutation, however, relational database schema mutation results in many “ineffective” mutants that both degrade test suite quality estimates and make mutation analysis more time consuming. This paper presents a taxonomy of ineffective mutants for relational database schemas, summarizing the root causes of ineffectiveness with a series of key patterns evident in database schemas. On the basis ... [more]


Reference

Phil McMinn, Chris J. Wright, Colton J. McCurdy, and Gregory M. Kapfhammer. Automatic Detection and Removal of Ineffective Mutants for the Mutation Analysis of Relational Database Schemas. IEEE Transactions on Software Engineering (To Appear), 2018


Bibtex Entry
@article{McMinn2018,
  author  = "McMinn, Phil and Wright, Chris J. and McCurdy, Colton J. and Kapfhammer, Gregory M.",
  title   = "Automatic Detection and Removal of Ineffective Mutants for the Mutation Analysis of Relational Database Schemas",
  journal = "IEEE Transactions on Software Engineering (To Appear)",
  year    = "2018"
}