You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at http://www.ieee.org/web/publications/rights/copyrightpolicy.html)
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at http://www.acm.org/pubs/copyright_policy/)
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact webmaster@ide.mdh.se

Searching for cognitively diverse tests: Towards universal test diversity metrics

Authors:


Publication Type:

Conference/Workshop Paper

Venue:

Proceedings of the 1st International Workshop on Search-based Software Testing (SBST'08), Collocated with 1st International Conference on Software Testing, Verification and Validation

DOI:

10.1109/ICSTW.2008.36


Abstract

Search-based software testing (SBST) has shown a potential to decrease cost and increase quality of testing related software development activities. Research in SBST has so far mainly focused on the search for isolated tests that are optimal according to a fitness function that guides the search. In this paper we make the case for fitness functions that measure test fitness in relation to existing or previously found tests; a test is good if it is diverse from other tests. We present a model for test variability and propose the use of a theoretically optimal diversity metric at variation points in the model. We then describe how to apply a practically useful approximation to the theoretically optimal metric. The metric is simple and powerful and can be adapted to a multitude of different test diversity measurement scenarios. We present initial results from an experiment to compare how similar to human subjects, the metric can cluster a set of test cases. To carry out the experiment we have extended an existing framework for test automation in an object-oriented, dynamic programming language.

Bibtex

@inproceedings{Feldt3119,
author = {Robert Feldt and Richard Torkar and Tony Gorschek and Wasif Afzal},
title = {Searching for cognitively diverse tests: Towards universal test diversity metrics},
month = {April},
year = {2011},
booktitle = {Proceedings of the 1st International Workshop on Search-based Software Testing (SBST'08), Collocated with 1st International Conference on Software Testing, Verification and Validation},
url = {http://www.es.mdu.se/publications/3119-}
}