research-article

Improved heuristics for solving OCL constraints using search algorithms

Authors:

Muhammad Zohaib Iqbal,

Andrea ArcuriAuthors Info & Claims

GECCO '14: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

Pages 1231 - 1238

https://doi.org/10.1145/2576768.2598308

Published: 12 July 2014 Publication History

Abstract

The Object Constraint Language (OCL) is a standard language for specifying constraints on Unified Modeling Language (UML) models. The specified constraints can be used for various purposes including verification, and model-based testing (e.g., test data generation). Efficiently solving OCL constraints is one of the key requirements for the practical use of OCL. In this paper, we propose an improvement in existing heuristics to solve OCL constraints using search algorithms. We evaluate our improved heuristics using two empirical studies with three search algorithms: Alternating Variable Method (AVM), (1+1) Evolutionary Algorithm (EA), and a Genetic Algorithm (GA). We also used Random Search (RS) as a comparison baseline. The first empirical study was conducted using carefully designed artificial problems (constraints) to assess each individual heuristics. The second empirical study is based on an industrial case study provided by Cisco about model-based testing of Video Conferencing Systems. The results of both empirical evaluations reveal that the effectiveness of the search algorithms, measured in terms of time to solve the OCL constraints to generate data, is significantly improved when using the novel heuristics presented in this paper. In particular, our experiments show that (1+1) EA with the novel heuristics has the highest success rate among all the analyzed algorithms, as it requires the least number of iterations to solve constraints.

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Cited By

Jilani AKhan MIqbal MUsman M(2022)An automated search‐based test model generation approach for structural testing of model transformationsJournal of Software: Evolution and Process10.1002/smr.246134:11Online publication date: 9-May-2022
https://doi.org/10.1002/smr.2461
Troya JSegura SParejo JRuiz-Cortés A(2018)Spectrum-Based Fault Localization in Model TransformationsACM Transactions on Software Engineering and Methodology10.1145/324174427:3(1-50)Online publication date: 17-Sep-2018
https://dl.acm.org/doi/10.1145/3241744
Ali SIqbal MKhalid MArcuri A(2018)Improving the performance of OCL constraint solving with novel heuristics for logical operationsEmpirical Software Engineering10.1007/s10664-015-9392-621:6(2459-2502)Online publication date: 26-Dec-2018
https://dl.acm.org/doi/10.1007/s10664-015-9392-6
Show More Cited By

Index Terms

Improved heuristics for solving OCL constraints using search algorithms
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction
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 '14: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

July 2014

1478 pages

ISBN:9781450326629

DOI:10.1145/2576768

Editor-in-chief:
Christian Igel
Ruhr University of Bochum, University of Copenhagen
,
General Chair:
Dirk V. Arnold
Dalhousie University

Copyright © 2014 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: 12 July 2014

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GECCO '14

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SIGEVO

GECCO '14: Genetic and Evolutionary Computation Conference

July 12 - 16, 2014

BC, Vancouver, Canada

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GECCO '14 Paper Acceptance Rate 180 of 544 submissions, 33%;

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

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Cited By

Jilani AKhan MIqbal MUsman M(2022)An automated search‐based test model generation approach for structural testing of model transformationsJournal of Software: Evolution and Process10.1002/smr.246134:11Online publication date: 9-May-2022
https://doi.org/10.1002/smr.2461
Troya JSegura SParejo JRuiz-Cortés A(2018)Spectrum-Based Fault Localization in Model TransformationsACM Transactions on Software Engineering and Methodology10.1145/324174427:3(1-50)Online publication date: 17-Sep-2018
https://dl.acm.org/doi/10.1145/3241744
Ali SIqbal MKhalid MArcuri A(2018)Improving the performance of OCL constraint solving with novel heuristics for logical operationsEmpirical Software Engineering10.1007/s10664-015-9392-621:6(2459-2502)Online publication date: 26-Dec-2018
https://dl.acm.org/doi/10.1007/s10664-015-9392-6
Ordoñez-Ordoñez PQuizhpe MCumbicus-Pineda OHerrera Salazar VFigueroa-Diaz R(2018)Application of Genetic Algorithms in Software Engineering: A Systematic Literature ReviewTechnology Trends10.1007/978-3-030-05532-5_50(659-670)Online publication date: 30-Dec-2018
https://doi.org/10.1007/978-3-030-05532-5_50
Ali SYue TQiu XLu H(2016)Generating boundary values from OCL constraints using constraints rewriting and search algorithms2016 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2016.7743819(379-386)Online publication date: Jul-2016
https://doi.org/10.1109/CEC.2016.7743819
Jilani AIqbal MKhan M(2014)A Search Based Test Data Generation Approach for Model TransformationsTheory and Practice of Model Transformations10.1007/978-3-319-08789-4_2(17-24)Online publication date: 2014
https://doi.org/10.1007/978-3-319-08789-4_2

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