Article

Lattice-based adaptive random testing

Author:

Johannes MayerAuthors Info & Claims

ASE '05: Proceedings of the 20th IEEE/ACM International Conference on Automated Software Engineering

Pages 333 - 336

https://doi.org/10.1145/1101908.1101963

Published: 07 November 2005 Publication History

Abstract

Adaptive Random Testing (ART) denotes a family of testing algorithms that have a better performance compared to pure random testing with respect to the number of test cases necessary to detect the first failure. Many of these algorithms, however, are not very efficient regarding runtime. A new ART algorithm is presented that has a better performance than all other ART methods for the block failure pattern. Its runtime is linear in the number of test cases selected, which is nearly as efficient as pure random testing, as opposed to most other ART methods. This new ART algorithm selects the test cases based on a lattice.

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Cited By

Chen JChen JZhu LMao CBao QHuang R(2024) L OP-ART: A linear-time adaptive random testing algorithm for object-oriented programs Journal of Systems and Software10.1016/j.jss.2024.111970211(111970)Online publication date: May-2024
https://doi.org/10.1016/j.jss.2024.111970
Gao XFeng YYin YLiu ZChen ZXu BDwyer MDamian DZeller A(2022)Adaptive test selection for deep neural networksProceedings of the 44th International Conference on Software Engineering10.1145/3510003.3510232(73-85)Online publication date: 21-May-2022
https://dl.acm.org/doi/10.1145/3510003.3510232
He CYang XLi MYan S(2022)Test case generation techniques based on isolation forest algorithmsInternational Conference on Mechanisms and Robotics (ICMAR 2022)10.1117/12.2653060(193)Online publication date: 10-Nov-2022
https://doi.org/10.1117/12.2653060
Show More Cited By

Index Terms

Lattice-based adaptive random testing
1. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms
      1. Survival analysis
2. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging

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Published In

cover image ACM Conferences

ASE '05: Proceedings of the 20th IEEE/ACM International Conference on Automated Software Engineering

November 2005

482 pages

ISBN:1581139934

DOI:10.1145/1101908

General Chair:
David Redmiles
University of California, Irvine, CA
,
Program Chairs:
Tom Ellman
Vassar College
,
Andrea Zisman
City University, UK

Copyright © 2005 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 November 2005

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ASE05: International Conference on Automated Software Engineering 2005

November 7 - 11, 2005

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Overall Acceptance Rate 82 of 337 submissions, 24%

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Cited By

Chen JChen JZhu LMao CBao QHuang R(2024) L OP-ART: A linear-time adaptive random testing algorithm for object-oriented programs Journal of Systems and Software10.1016/j.jss.2024.111970211(111970)Online publication date: May-2024
https://doi.org/10.1016/j.jss.2024.111970
Gao XFeng YYin YLiu ZChen ZXu BDwyer MDamian DZeller A(2022)Adaptive test selection for deep neural networksProceedings of the 44th International Conference on Software Engineering10.1145/3510003.3510232(73-85)Online publication date: 21-May-2022
https://dl.acm.org/doi/10.1145/3510003.3510232
He CYang XLi MYan S(2022)Test case generation techniques based on isolation forest algorithmsInternational Conference on Mechanisms and Robotics (ICMAR 2022)10.1117/12.2653060(193)Online publication date: 10-Nov-2022
https://doi.org/10.1117/12.2653060
Chen BLiu YPeng XWu YQin S(2022)Baton: symphony of random testing and concolic testing through machine learning and taint analysisScience China Information Sciences10.1007/s11432-020-3403-266:3Online publication date: 11-Nov-2022
https://doi.org/10.1007/s11432-020-3403-2
Huang RSun WXu YChen HTowey DXia X(2021)A Survey on Adaptive Random TestingIEEE Transactions on Software Engineering10.1109/TSE.2019.294292147:10(2052-2083)Online publication date: 1-Oct-2021
https://doi.org/10.1109/TSE.2019.2942921
Pei HCai KYin BMathur AXie M(2019)Dynamic Random Testing: Technique and Experimental EvaluationIEEE Transactions on Reliability10.1109/TR.2019.291159368:3(872-892)Online publication date: Sep-2019
https://doi.org/10.1109/TR.2019.2911593
Mao CZhan XTse TChen T(2019)KDFC-ART: a KD-tree approach to enhancing Fixed-size-Candidate-set Adaptive Random TestingIEEE Transactions on Reliability10.1109/TR.2019.289223068:4(1444-1469)Online publication date: Dec-2019
https://doi.org/10.1109/TR.2019.2892230
Diao XRodriguez MLi BSmidts C(2018)Automated Software TestingAnalytic Methods in Systems and Software Testing10.1002/9781119357056.ch15(373-403)Online publication date: 6-Jul-2018
https://doi.org/10.1002/9781119357056.ch15
Löscher ASagonas KBultan TSen K(2017)Targeted property-based testingProceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3092703.3092711(46-56)Online publication date: 10-Jul-2017
https://dl.acm.org/doi/10.1145/3092703.3092711
Chen JKuo FChen TTowey DSu CHuang R(2017)A Similarity Metric for the Inputs of OO Programs and Its Application in Adaptive Random TestingIEEE Transactions on Reliability10.1109/TR.2016.262875966:2(373-402)Online publication date: Jun-2017
https://doi.org/10.1109/TR.2016.2628759
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