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Search-based software test data generation: a survey: Research Articles

Published: 01 June 2004 Publication History

Abstract

The use of metaheuristic search techniques for the automatic generation of test data has been a burgeoning interest for many researchers in recent years. Previous attempts to automate the test generation process have been limited, having been constrained by the size and complexity of software, and the basic fact that, in general, test data generation is an undecidable problem. Metaheuristic search techniques offer much promise in regard to these problems. Metaheuristic search techniques are high-level frameworks, which utilize heuristics to seek solutions for combinatorial problems at a reasonable computational cost. To date, metaheuristic search techniques have been applied to automate test data generation for structural and functional testing; the testing of grey-box properties, for example safety constraints; and also non-functional properties, such as worst-case execution time. This paper surveys some of the work undertaken in this field, discussing possible new future directions of research for each of its different individual areas. Copyright © 2004 John Wiley & Sons, Ltd.

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  • (2024)Optimizing Search-Based Unit Test Generation with Large Language Models: An Empirical StudyProceedings of the 15th Asia-Pacific Symposium on Internetware10.1145/3671016.3674813(71-80)Online publication date: 24-Jul-2024
  • (2024)Syntest-JavaScript: Automated Unit-Level Test Case Generation for JavaScriptProceedings of the 17th ACM/IEEE International Workshop on Search-Based and Fuzz Testing10.1145/3643659.3643928(21-24)Online publication date: 14-Apr-2024
  • (2024)Enhancing Testing at Meta with Rich-State Simulated PopulationsProceedings of the 46th International Conference on Software Engineering: Software Engineering in Practice10.1145/3639477.3639729(1-12)Online publication date: 14-Apr-2024
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    Published In

    cover image Software Testing, Verification & Reliability
    Software Testing, Verification & Reliability  Volume 14, Issue 2
    June 2004
    81 pages

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    John Wiley and Sons Ltd.

    United Kingdom

    Publication History

    Published: 01 June 2004

    Author Tags

    1. automated software test data generation
    2. evolutionary algorithms
    3. evolutionary testing
    4. metaheuristic search
    5. search-based software engineering
    6. simulated annealing

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    • (2024)Optimizing Search-Based Unit Test Generation with Large Language Models: An Empirical StudyProceedings of the 15th Asia-Pacific Symposium on Internetware10.1145/3671016.3674813(71-80)Online publication date: 24-Jul-2024
    • (2024)Syntest-JavaScript: Automated Unit-Level Test Case Generation for JavaScriptProceedings of the 17th ACM/IEEE International Workshop on Search-Based and Fuzz Testing10.1145/3643659.3643928(21-24)Online publication date: 14-Apr-2024
    • (2024)Enhancing Testing at Meta with Rich-State Simulated PopulationsProceedings of the 46th International Conference on Software Engineering: Software Engineering in Practice10.1145/3639477.3639729(1-12)Online publication date: 14-Apr-2024
    • (2024)Search-based Crash Reproduction for Android AppsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654034(1426-1434)Online publication date: 14-Jul-2024
    • (2024)Boundary State Generation for Testing and Improvement of Autonomous Driving SystemsIEEE Transactions on Software Engineering10.1109/TSE.2024.342081650:8(2040-2053)Online publication date: 1-Aug-2024
    • (2024)GenMorph: Automatically Generating Metamorphic Relations via Genetic ProgrammingIEEE Transactions on Software Engineering10.1109/TSE.2024.340784050:7(1888-1900)Online publication date: 1-Jul-2024
    • (2024)Toward Cost-Effective Adaptive Random Testing: An Approximate Nearest Neighbor ApproachIEEE Transactions on Software Engineering10.1109/TSE.2024.337959250:5(1182-1214)Online publication date: 21-Mar-2024
    • (2024)Coverage Goal Selector for Combining Multiple Criteria in Search-Based Unit Test GenerationIEEE Transactions on Software Engineering10.1109/TSE.2024.336661350:4(854-883)Online publication date: 16-Feb-2024
    • (2023)Demystify the Fuzzing Methods: A Comprehensive SurveyACM Computing Surveys10.1145/362337556:3(1-38)Online publication date: 5-Oct-2023
    • (2023)JavaScript SBST Heuristics to Enable Effective Fuzzing of NodeJS Web APIsACM Transactions on Software Engineering and Methodology10.1145/359380132:6(1-29)Online publication date: 24-Apr-2023
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