research-article

A Hyper-Heuristic for the Multi-Objective Integration and Test Order Problem

Authors:

Giovani Guizzo,

Gian Mauricio Fritsche,

Silvia Regina Vergilio,

Aurora Trinidad Ramirez PozoAuthors Info & Claims

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

Pages 1343 - 1350

https://doi.org/10.1145/2739480.2754725

Published: 11 July 2015 Publication History

Abstract

Multi-objective evolutionary algorithms (MOEAs) have been efficiently applied to Search-Based Software Engineering (SBSE) problems. However, skilled software engineers waste significant effort designing such algorithms for a particular problem, adapting them, selecting operators and configuring parameters. Hyper-heuristics can help in these tasks by dynamically selecting or creating heuristics. Despite of such advantages, we observe a lack of works regarding this subject in the SBSE field. Considering this fact, this work introduces HITO, a Hyper-heuristic for the Integration and Test Order Problem. It includes a set of well-defined steps and is based on two selection functions (Choice Function and Multi-armed Bandit) to select the best low-level heuristic (combination of mutation and crossover operators) in each mating. To perform the selection, a quality measure is proposed to assess the performance of low-level heuristics throughout the evolutionary process. HITO was implemented using NSGA-II and evaluated to solve the integration and test order problem in seven systems. The introduced hyper-heuristic obtained the best results for all systems, when compared to a traditional algorithm.

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Dokeroglu TKucukyilmaz TTalbi E(2024)Hyper-heuristics: A survey and taxonomyComputers & Industrial Engineering10.1016/j.cie.2023.109815187(109815)Online publication date: Jan-2024
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Zhang SYang TLiang JYue C(2023)A Novel Adaptive Bandit-Based Selection Hyper-Heuristic for Multiobjective OptimizationIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2023.329998253:12(7693-7706)Online publication date: Dec-2023
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Index Terms

A Hyper-Heuristic for the Multi-Objective Integration and Test Order Problem

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Published In

cover image ACM Conferences

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

July 2015

1496 pages

ISBN:9781450334723

DOI:10.1145/2739480

Editor:
Sara Silva
Universidade de Lisboa, Portugal
,
General Chair:
Anna I. Esparcia-Alcázar
Universitat Politècnica de València, Spain

Copyright © 2015 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

Publication History

Published: 11 July 2015

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Research-article

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Brazilian National Council for Scientific and Technological Development (CNPq)

Conference

GECCO '15

Sponsor:

SIGEVO

GECCO '15: Genetic and Evolutionary Computation Conference

July 11 - 15, 2015

Madrid, Spain

Acceptance Rates

GECCO '15 Paper Acceptance Rate 182 of 505 submissions, 36%;

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

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Cited By

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https://doi.org/10.1016/j.aei.2024.102974
Dokeroglu TKucukyilmaz TTalbi E(2024)Hyper-heuristics: A survey and taxonomyComputers & Industrial Engineering10.1016/j.cie.2023.109815187(109815)Online publication date: Jan-2024
https://doi.org/10.1016/j.cie.2023.109815
Zhang SYang TLiang JYue C(2023)A Novel Adaptive Bandit-Based Selection Hyper-Heuristic for Multiobjective OptimizationIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2023.329998253:12(7693-7706)Online publication date: Dec-2023
https://doi.org/10.1109/TSMC.2023.3299982
Derakhshanfar PDevroey XPanichella AZaidman Avan Deursen A(2023)Generating Class-Level Integration Tests Using Call Site InformationIEEE Transactions on Software Engineering10.1109/TSE.2022.320962549:4(2069-2087)Online publication date: 1-Apr-2023
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Ji JGuo YGao XGong DWang Y(2023) Q -Learning-Based Hyperheuristic Evolutionary Algorithm for Dynamic Task Allocation of Crowdsensing IEEE Transactions on Cybernetics10.1109/TCYB.2021.311267553:4(2211-2224)Online publication date: Apr-2023
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Ding YZhang YYuan GJiang SDai WZhang Y(2023)Integration test order generation based on reinforcement learning considering class importanceJournal of Systems and Software10.1016/j.jss.2023.111823205:COnline publication date: 17-Oct-2023
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Li YZhang YDing YJiang SYuan G(2023)A class integration test order generation approach based on Sarsa algorithmAutomated Software Engineering10.1007/s10515-023-00406-931:1Online publication date: 13-Dec-2023
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Guo JWang RHan JLi Z(2023)Research on hyper‐level of hyper‐heuristic framework for MOTCPSoftware Testing, Verification and Reliability10.1002/stvr.186133:8Online publication date: 3-Sep-2023
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Zhang YJiang SDing YYuan GLiu JLu DQian J(2022)Generating Optimal Class Integration Test Orders Using Genetic AlgorithmsInternational Journal of Software Engineering and Knowledge Engineering10.1142/S021819402250030932:06(871-892)Online publication date: 11-Jun-2022
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Tawosi VSarro FPetrozziello AHarman M(2022)Multi-Objective Software Effort Estimation: A Replication StudyIEEE Transactions on Software Engineering10.1109/TSE.2021.308336048:8(3185-3205)Online publication date: 1-Aug-2022
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