Automatically Evaluating the Efficiency of Search-Based Test Data Generation for Relational Database Schemas

by Cody Kinneer, Gregory M. Kapfhammer, Chris J. Wright and Phil McMinn

International Conference on Software Engineering and Knowledge Engineering (SEKE 2015)



The characterization of an algorithm’s worst-case time complexity is useful because it succinctly captures how its runtime will grow as the input size becomes arbitrarily large. However, for certain algorithms—such as those performing search-based test data generation—a theoretical analysis to determine worst-case time complexity is difficult to generalize and thus not often reported in the literature. This paper introduces a framework that empirically determines an algorithm’s worst-case time complexity by doubling the size of the input and observing the change in runtime. Since the relational database is a centerpiece of modern software and the database’s schema is frequently untested, we ... [more]


Reference

Cody Kinneer, Gregory M. Kapfhammer, Chris J. Wright and Phil McMinn. Automatically Evaluating the Efficiency of Search-Based Test Data Generation for Relational Database Schemas. International Conference on Software Engineering and Knowledge Engineering (SEKE 2015), 2015


Bibtex Entry
@inproceedings{Kinneer2015,
  author    = "Kinneer, Cody and Kapfhammer, Gregory M. and Wright, Chris J. and McMinn, Phil",
  title     = "Automatically Evaluating the Efficiency of Search-Based Test Data Generation for Relational Database Schemas",
  booktitle = "International Conference on Software Engineering and Knowledge Engineering (SEKE 2015)",
  year      = "2015"
}