Feldt, R;
Poulding, S;
Clark, D;
Yoo, S;
(2016)
Test Set Diameter: Quantifying the Diversity of Sets of Test Cases.
In: Grechanik, M, (ed.)
2016 IEEE International Conference on Software Testing, Verification and Validation: Proceedings.
(pp. pp. 223-233).
Institute of Electrical and Electronics Engineers (IEEE)
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Abstract
A common and natural intuition among software testers is that test cases need to differ if a software system is to be tested properly and its quality ensured. Consequently, much research has gone into formulating distance measures for how test cases, their inputs and/or their outputs differ. However, common to these proposals is that they are data type specific and/or calculate the diversity only between pairs of test inputs, traces or outputs. We propose a new metric to measure the diversity of sets of tests: the test set diameter (TSDm). It extends our earlier, pairwise test diversity metrics based on recent advances in information theory regarding the calculation of the normalized compression distance (NCD) for multisets. A key advantage is that TSDm is a universal measure of diversity and so can be applied to any test set regardless of data type of the test inputs (and, moreover, to other test-related data such as execution traces). But this universality comes at the cost of greater computational effort compared to competing approaches. Our experiments on four different systems show that the test set diameter can help select test sets with higher structural and fault coverage than random selection even when only applied to test inputs. This can enable early test design and selection, prior to even having a software system to test, and complement other types of test automation and analysis. We argue that this quantification of test set diversity creates a number of opportunities to better understand software quality and provides practical ways to increase it.
| Type: | Proceedings paper |
|---|---|
| Title: | Test Set Diameter: Quantifying the Diversity of Sets of Test Cases |
| Event: | ICST 2016: IEEE International Conference on Software Testing, Verification and Validation, 11-15 April 2016, Chicago, Illinois, USA |
| ISBN-13: | 9781509018284 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ICST.2016.33 |
| Publisher version: | http://dx.doi.org/10.1109/ICST.2016.33 |
| Language: | English |
| Additional information: | Copyright © 2016 by the Institute of Electrical and Electronics Engineers, Inc. All Rights Reserved. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Keywords: | Empirical study, Software testing, Information theory, Test selection |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1517700 |
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