tutorial

Generation of tests for programming challenge tasks using evolution algorithms

Author:

Maxim BuzdalovAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation

Pages 763 - 766

https://doi.org/10.1145/2001858.2002086

Published: 12 July 2011 Publication History

Abstract

In this paper, an automated method for generation of tests in order to detect inefficient (slow) solutions for programming challenge tasks is proposed. The method is based on genetic algorithms.

The proposed method was applied to a task from the Internet problem archive - the Timus Online Judge. For this problem, none of the existed solutions passed the generated set of tests.

References

[1]

ACM International Collegiate Programming Contest. http://cm.baylor.edu/welcome.icpc.

[2]

International Olympiad in Informatics. http://www.ioinformatics.org.

[3]

NEERC Contest Rules. http://neerc.ifmo.ru/information/contest-rules.html.

[4]

Problem "Ships. Version 2". http://acm.timus.ru/problem.aspx?num=1394.

[5]

Programming Contests at TopCoder. http://www.topcoder.com/tc.

[6]

Timus Online Judge. The Problem Archive with Online Judge System. http://acm.timus.ru.

[7]

J. T. Alander, T. Mantere, and P. Turunen. Genetic Algorithm Based Software Testing. In Artificial Neural Nets and Genetic Algorithms, pages 325--328, Wien, Austria, 1998. Springer-Verlag.

[8]

K. Beck. Extreme Programming Explained: Embrace Change. Addison-Wesley Professional, 1999.

Digital Library

[9]

V. Chvatal. Hard Knapsack Problems. Operations Research, 28(6):1402--1411, 1980.

[10]

J. R. Koza. Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge, MA, USA, 1992.

Digital Library

[11]

M. Mitchell. An Introduction to Genetic Algorithms. MIT Press, Cambridge, MA, 1996.

Digital Library

[12]

G. J. Myers. The Art of Software Testing, Second Edition. John Wiley & Sons, Inc., 2004.

Digital Library

[13]

D. Pisinger. Algorithms for Knapsack Problems. PhD thesis, University of Copenhagen, February 1995.

[14]

S. S. Skiena and M. A. Revilla. Programming Challenges: The Programming Contest Training Manual. Springer Verlag, New York, 2003.

Digital Library

[15]

P. Tonella. Evolutionary testing of classes. In ISSTA, pages 119--128, 2004.

Digital Library

Cited By

Teran-Pomier J(2016)De las competencias de programación a una pedagogía educativa en La Paz BoliviaInnoeduca. International Journal of Technology and Educational Innovation10.20548/innoeduca.2016.v2i2.20342:2(128-137)Online publication date: 27-Nov-2016
https://doi.org/10.20548/innoeduca.2016.v2i2.2034
Buzdalov MShalyto A(2015)Hard test generation for augmenting path maximum flow algorithms using genetic algorithms: Revisited2015 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2015.7257146(2121-2128)Online publication date: May-2015
https://doi.org/10.1109/CEC.2015.7257146
Buzdalov MBuzdalova APetrova IAlba E(2013)Generation of tests for programming challenge tasks using multi-objective optimizationProceedings of the 15th annual conference companion on Genetic and evolutionary computation10.1145/2464576.2482746(1655-1658)Online publication date: 6-Jul-2013
https://dl.acm.org/doi/10.1145/2464576.2482746
Show More Cited By

Index Terms

Generation of tests for programming challenge tasks using evolution algorithms
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging

Recommendations

Generation of tests for programming challenge tasks using multi-objective optimization
GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation

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. ...
Generation of Tests for Programming Challenge Tasks Using Helper-Objectives
SSBSE 2013: Proceedings of the 5th International Symposium on Search Based Software Engineering - Volume 8084

Generation of performance tests for programming challenge tasks is considered. A number of evolutionary approaches are compared on two different solutions of an example problem. It is shown that using helper-objectives enhances evolutionary algorithms ...
Neural network crossover in genetic algorithms using genetic programming
Abstract
The use of genetic algorithms (GAs) to evolve neural network (NN) weights has risen in popularity in recent years, particularly when used together with gradient descent as a mutation operator. However, crossover operators are often omitted from ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation

July 2011

1548 pages

ISBN:9781450306904

DOI:10.1145/2001858

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

Copyright © 2011 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]

Sponsors

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2011

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Tutorial

Conference

GECCO '11

Sponsor:

SIGEVO

GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
109
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Teran-Pomier J(2016)De las competencias de programación a una pedagogía educativa en La Paz BoliviaInnoeduca. International Journal of Technology and Educational Innovation10.20548/innoeduca.2016.v2i2.20342:2(128-137)Online publication date: 27-Nov-2016
https://doi.org/10.20548/innoeduca.2016.v2i2.2034
Buzdalov MShalyto A(2015)Hard test generation for augmenting path maximum flow algorithms using genetic algorithms: Revisited2015 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2015.7257146(2121-2128)Online publication date: May-2015
https://doi.org/10.1109/CEC.2015.7257146
Buzdalov MBuzdalova APetrova IAlba E(2013)Generation of tests for programming challenge tasks using multi-objective optimizationProceedings of the 15th annual conference companion on Genetic and evolutionary computation10.1145/2464576.2482746(1655-1658)Online publication date: 6-Jul-2013
https://dl.acm.org/doi/10.1145/2464576.2482746
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
Arkhipov VBuzdalov MShalyto A(2013)Worst-Case Execution Time Test Generation for Augmenting Path Maximum Flow Algorithms Using Genetic AlgorithmsProceedings of the 2013 12th International Conference on Machine Learning and Applications - Volume 0210.1109/ICMLA.2013.180(108-111)Online publication date: 4-Dec-2013
https://dl.acm.org/doi/10.1109/ICMLA.2013.180
Buzdalov MBuzdalova A(2013)Adaptive selection of helper-objectives for test case generation2013 IEEE Congress on Evolutionary Computation10.1109/CEC.2013.6557836(2245-2250)Online publication date: Jun-2013
https://doi.org/10.1109/CEC.2013.6557836
Buzdalova ABuzdalov MParfenov V(2013)Generation of Tests for Programming Challenge Tasks Using Helper-ObjectivesProceedings of the 5th International Symposium on Search Based Software Engineering - Volume 808410.1007/978-3-642-39742-4_28(300-305)Online publication date: 24-Aug-2013
https://dl.acm.org/doi/10.1007/978-3-642-39742-4_28
Buzdalova ABuzdalov M(2012)Increasing Efficiency of Evolutionary Algorithms by Choosing between Auxiliary Fitness Functions with Reinforcement LearningProceedings of the 2012 11th International Conference on Machine Learning and Applications - Volume 0110.1109/ICMLA.2012.32(150-155)Online publication date: 12-Dec-2012
https://dl.acm.org/doi/10.1109/ICMLA.2012.32
Buzdalov M(2012)Generation of Tests for Programming Challenge Tasks on Graph Theory Using Evolution StrategyProceedings of the 2012 11th International Conference on Machine Learning and Applications - Volume 0210.1109/ICMLA.2012.194(62-65)Online publication date: 12-Dec-2012
https://dl.acm.org/doi/10.1109/ICMLA.2012.194
Afanasyeva ABuzdalov M(2011)Choosing Best Fitness Function with Reinforcement LearningProceedings of the 2011 10th International Conference on Machine Learning and Applications and Workshops - Volume 0210.1109/ICMLA.2011.163(354-357)Online publication date: 18-Dec-2011
https://dl.acm.org/doi/10.1109/ICMLA.2011.163

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten