survey

Using Genetic Algorithms in Test Data Generation: A Critical Systematic Mapping

Authors:

Davi Silva Rodrigues,

Márcio Eduardo Delamaro,

Cléber Gimenez Corrêa,

Fátima L. S. NunesAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 51, Issue 2

Article No.: 41, Pages 1 - 23

https://doi.org/10.1145/3182659

Published: 22 May 2018 Publication History

Abstract

Software testing activities account for a considerable portion of systems development cost and, for this reason, many studies have sought to automate these activities. Test data generation has a high cost reduction potential (especially for complex domain systems), since it can decrease human effort. Although several studies have been published about this subject, articles of reviews covering this topic usually focus only on specific domains. This article presents a systematic mapping aiming at providing a broad, albeit critical, overview of the literature in the topic of test data generation using genetic algorithms. The selected studies were categorized by software testing technique (structural, functional, or mutation testing) for which test data were generated and according to the most significantly adapted genetic algorithms aspects. The most used evaluation metrics and software testing techniques were identified. The results showed that genetic algorithms have been successfully applied to simple test data generation, but are rarely used to generate complex test data such as images, videos, sounds, and 3D (three-dimensional) models. From these results, we discuss some challenges and opportunities for research in this area.

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Index Terms

Using Genetic Algorithms in Test Data Generation: A Critical Systematic Mapping
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Bio-inspired approaches
        Genetic algorithms
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 Computing Surveys

ACM Computing Surveys Volume 51, Issue 2

March 2019

748 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3186333

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Sartaj Sahni
Department of Computer and Information Science and Engineering / University of Florida / Gainesville, FL 32611

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Publication History

Published: 22 May 2018

Accepted: 01 January 2018

Revised: 01 January 2018

Received: 01 January 2017

Published in CSUR Volume 51, Issue 2

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