article

Evolutionary testing of classes

Author:

Paolo TonellaAuthors Info & Claims

ACM SIGSOFT Software Engineering Notes, Volume 29, Issue 4

Pages 119 - 128

https://doi.org/10.1145/1013886.1007528

Published: 01 July 2004 Publication History

Abstract

Object oriented programming promotes reuse of classes in multiple contexts. Thus, a class is designed and implemented with several usage scenarios in mind, some of which possibly open and generic. Correspondingly, the unit testing of classes cannot make too strict assumptions on the actual method invocation sequences, since these vary from application to application.In this paper, a genetic algorithm is exploited to automatically produce test cases for the unit testing of classes in a generic usage scenario. Test cases are described by chromosomes, which include information on which objects to create, which methods to invoke and which values to use as inputs. The proposed algorithm mutates them with the aim of maximizing a given coverage measure. The implementation of the algorithm and its application to classes from the Java standard library are described.

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Cited By

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https://doi.org/10.1007/s10515-025-00496-7
Bhatia SGandhi TKumar DJalote P(2024)Unit Test Generation using Generative AI : A Comparative Performance Analysis of Autogeneration ToolsProceedings of the 1st International Workshop on Large Language Models for Code10.1145/3643795.3648396(54-61)Online publication date: 20-Apr-2024
https://dl.acm.org/doi/10.1145/3643795.3648396
Cerny TAbdelfattah AYero JTaibi D(2024)From static code analysis to visual models of microservice architectureCluster Computing10.1007/s10586-024-04394-727:4(4145-4170)Online publication date: 24-Apr-2024
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Index Terms

Evolutionary testing of classes
1. 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 SIGSOFT Software Engineering Notes

ACM SIGSOFT Software Engineering Notes Volume 29, Issue 4

July 2004

284 pages

ISSN:0163-5948

DOI:10.1145/1013886

Issue’s Table of Contents

ISSTA '04: Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis
July 2004
294 pages
ISBN:1581138202
DOI:10.1145/1007512
General Chair:
George Avrunin
University of Massachusetts, USA
,
Program Chair:
Gregg Rothermel
University of Nebraska -- Lincoln, USA

Copyright © 2004 ACM.

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Published: 01 July 2004

Published in SIGSOFT Volume 29, Issue 4

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Cited By

Yang RXu XWang R(2025)LLM-enhanced evolutionary test generation for untyped languagesAutomated Software Engineering10.1007/s10515-025-00496-732:1Online publication date: 17-Feb-2025
https://doi.org/10.1007/s10515-025-00496-7
Bhatia SGandhi TKumar DJalote P(2024)Unit Test Generation using Generative AI : A Comparative Performance Analysis of Autogeneration ToolsProceedings of the 1st International Workshop on Large Language Models for Code10.1145/3643795.3648396(54-61)Online publication date: 20-Apr-2024
https://dl.acm.org/doi/10.1145/3643795.3648396
Cerny TAbdelfattah AYero JTaibi D(2024)From static code analysis to visual models of microservice architectureCluster Computing10.1007/s10586-024-04394-727:4(4145-4170)Online publication date: 24-Apr-2024
https://dl.acm.org/doi/10.1007/s10586-024-04394-7
Ahsan FAnwer F(2024)A systematic literature review on software security testing using metaheuristicsAutomated Software Engineering10.1007/s10515-024-00433-031:2Online publication date: 23-May-2024
https://dl.acm.org/doi/10.1007/s10515-024-00433-0
Zhi YXie XShen CSun JZhang XGuan X(2023)Seed Selection for Testing Deep Neural NetworksACM Transactions on Software Engineering and Methodology10.1145/360719033:1(1-33)Online publication date: 7-Jul-2023
https://dl.acm.org/doi/10.1145/3607190
Applis LPanichella AMarang RSilva SPaquete L(2023)Searching for Quality: Genetic Algorithms and Metamorphic Testing for Software Engineering MLProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590379(1490-1498)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590379
Neelofar NSmith-Miles KMuñoz MAleti A(2023)Instance Space Analysis of Search-Based Software TestingIEEE Transactions on Software Engineering10.1109/TSE.2022.322833449:4(2642-2660)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1109/TSE.2022.3228334
Zakeriyan AKhosravi RSafari HKhamespanah EShamsabadi S(2023)Automated testing of an industrial stock market trading platform based on functional specificationScience of Computer Programming10.1016/j.scico.2022.102908225:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.scico.2022.102908
Feldmeier PFraser G(2022)Neuroevolution-Based Generation of Tests and Oracles for GamesProceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering10.1145/3551349.3556939(1-13)Online publication date: 10-Oct-2022
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Olsthoorn MDwyer M(2022)More effective test case generation with multiple tribes of AIProceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings10.1145/3510454.3517066(286-290)Online publication date: 21-May-2022
https://dl.acm.org/doi/10.1145/3510454.3517066
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