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Generation of tests for programming challenge tasks using multi-objective optimization

Authors:

Maxim Buzdalov,

Arina Buzdalova,

Irina PetrovaAuthors Info & Claims

GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation

Pages 1655 - 1658

https://doi.org/10.1145/2464576.2482746

Published: 06 July 2013 Publication History

Abstract

In this paper, an evolutionary approach to generation of test cases for programming challenge tasks is investigated. Multi-objective and single-objective evolutionary algorithms, as well as helper-objective selection strategies, are compared. Particularly, a previously proposed method of choosing between helper-objectives with reinforcement learning is considered. This method is applied to the multi-objective evolutionary algorithm for the first time. Results of the experiment show that the most reasonable approach for the considered problem is using multi-objective evolutionary algorithm with automated helper-objective selection.

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Cited By

Nayab SWotawa F(2024)Testing and Reinforcement Learning - A Structured Literature Review2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C)10.1109/QRS-C63300.2024.00049(326-335)Online publication date: 1-Jul-2024
https://doi.org/10.1109/QRS-C63300.2024.00049
Abo-eleneen APalliyali ACatal C(2024)The role of Reinforcement Learning in software testingInformation and Software Technology10.1016/j.infsof.2023.107325164:COnline publication date: 10-Jan-2024
https://dl.acm.org/doi/10.1016/j.infsof.2023.107325
Antipov DBuzdalova AStankevich ATakadama K(2018)Runtime analysis of a population-based evolutionary algorithm with auxiliary objectives selected by reinforcement learningProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3208231(1886-1889)Online publication date: 6-Jul-2018
https://dl.acm.org/doi/10.1145/3205651.3208231
Show More Cited By

Index Terms

Generation of tests for programming challenge tasks using multi-objective optimization
1. Computing methodologies
  1. Machine learning
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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cover image ACM Conferences

GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation

July 2013

1798 pages

ISBN:9781450319645

DOI:10.1145/2464576

Editor:
Christian Blum
IKERBASQUE and University of the Basque Country UPV/EHU, Spain
,
General Chair:
Enrique Alba
University of Malaga, Spain

Copyright © 2013 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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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

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Published: 06 July 2013

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GECCO '13

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SIGEVO

GECCO '13: Genetic and Evolutionary Computation Conference

July 6 - 10, 2013

Amsterdam, The Netherlands

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Nayab SWotawa F(2024)Testing and Reinforcement Learning - A Structured Literature Review2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C)10.1109/QRS-C63300.2024.00049(326-335)Online publication date: 1-Jul-2024
https://doi.org/10.1109/QRS-C63300.2024.00049
Abo-eleneen APalliyali ACatal C(2024)The role of Reinforcement Learning in software testingInformation and Software Technology10.1016/j.infsof.2023.107325164:COnline publication date: 10-Jan-2024
https://dl.acm.org/doi/10.1016/j.infsof.2023.107325
Antipov DBuzdalova AStankevich ATakadama K(2018)Runtime analysis of a population-based evolutionary algorithm with auxiliary objectives selected by reinforcement learningProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3208231(1886-1889)Online publication date: 6-Jul-2018
https://dl.acm.org/doi/10.1145/3205651.3208231
Antipov DBuzdalova A(2017)Runtime Analysis of Random Local Search on JUMP function with Reinforcement Based Selection of Auxiliary Objectives2017 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2017.7969567(2169-2176)Online publication date: Jun-2017
https://doi.org/10.1109/CEC.2017.7969567
Buzdalova APetrova IBuzdalov M(2016)Runtime analysis of different Approaches to select conflicting auxiliary objectives in the generalized OneMax problem2016 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI.2016.7850140(1-7)Online publication date: Dec-2016
https://doi.org/10.1109/SSCI.2016.7850140
Buzdalova AMatveeva AKorneev GEsparcia-Alcázar A(2015)Selection of Auxiliary Objectives with Multi-Objective Reinforcement LearningProceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation10.1145/2739482.2768473(1177-1180)Online publication date: 11-Jul-2015
https://dl.acm.org/doi/10.1145/2739482.2768473
Segura CCoello CMiranda GLeón C(2015)Using multi-objective evolutionary algorithms for single-objective constrained and unconstrained optimizationAnnals of Operations Research10.1007/s10479-015-2017-z240:1(217-250)Online publication date: 22-Sep-2015
https://doi.org/10.1007/s10479-015-2017-z
Buzdalova AKononov VBuzdalov MArnold D(2014)Selecting evolutionary operators using reinforcement learningProceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2598394.2605681(1033-1036)Online publication date: 12-Jul-2014
https://dl.acm.org/doi/10.1145/2598394.2605681
Potena PFernandez-Sanz LPages CDiez T(2014)Creating a Framework for Quality Decisions in Software ProjectsComputational Science and Its Applications – ICCSA 201410.1007/978-3-319-09156-3_31(434-448)Online publication date: 2014
https://doi.org/10.1007/978-3-319-09156-3_31
Buzdalov MBuzdalova AShalyto A(2013)A First Step towards the Runtime Analysis of Evolutionary Algorithm Adjusted with Reinforcement LearningProceedings of the 2013 12th International Conference on Machine Learning and Applications - Volume 0110.1109/ICMLA.2013.42(203-208)Online publication date: 4-Dec-2013
https://dl.acm.org/doi/10.1109/ICMLA.2013.42
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