Swinburne
Browse

On test case distributions of adaptive random testing

Download (287.6 kB)
conference contribution
posted on 2024-07-11, 11:36 authored by Tsong ChenTsong Chen, Fei-Ching Kuo, Huai LiuHuai Liu
Adaptive Random Testing (ART) has recently been proposed as an approach to enhancing the fault-detection effectiveness of Random Testing (RT). The basic principle of ART is to enforce randomly selected test cases as evenly spread over the input domain as possible. Many ART methods have been proposed to evenly spread test cases in different ways, but no comparison has been made among these methods in terms of their test case distributions. In this paper, we conduct a comprehensive investigation on test case distributions of various ART methods. Our work shows many interesting aspects related to ART’s performance and its test case distribution. Furthermore, it points out a new research direction on enhancing ART.

Funding

Enhanced Random Testing - Towards Better Cost Effectiveness and Fault Detection Capabilities

Australian Research Council

Find out more...

History

Available versions

PDF (Published version)

ISBN

9781627486613

Conference name

19th International Conference on Software Engineering and Knowledge Engineering, SEKE 2007

Pagination

3 pp

Publisher

Knowledge Systems Institute Graduate School

Copyright statement

Copyright © 2007 Knowledge Systems Institute. The author retains the right to reproduce the paper for personal use or for company use provided that (a) the source and KSI copyright are included, (b) the copies are not used in a way that implies KSI endorsement of a product or service of an employer, and (c) the copies per se are not offered for sale. The published version is reproduced in accordance with this policy.

Language

eng

Usage metrics

    Publications

    Categories

    No categories selected

    Keywords

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC