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Automatic Test Data Generation Using a Genetic Algorithm

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Computational Science and Its Applications – ICCSA 2013 (ICCSA 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7972))

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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. In this paper, we present a new evolutionary approach for automated test data generation for structural testing. Our method presents several noteworthy features: It uses a newly defined program modeling allowing an easy program manipulation. Furthermore, instead of affecting a unique value for each input variable, we assign to each input an interval. This representation has the advantage of delimiting first the input value and to refine the interval progressively. In this manner, the search space is explored more efficiently. We use an original fitness function, which expresses truthfully the individual quality. Furthermore, we define a crossover operator allowing to effectively improving individuals.

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Authors and Affiliations

  1. Computer Sciences Department, University of Sciences and Technologies Houari Boumediene, BP 32, El Alia, Bab Ezzouar, 16111, Algiers, Algeria

    Nassima Aleb & Samir Kechid

Authors
  1. Nassima Aleb
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  2. Samir Kechid
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Editor information

Editors and Affiliations

  1. L-I.S.U.T. - D.A.P.I.t. Facoltà Ingegneria, Università degli Studi della Basilicata, Viale dell’Ateneo Lucano, 10, 85100, Potenza, Italy

    Beniamino Murgante

  2. Covenant University, Canaanland, OTA, Nigeria

    Sanjay Misra

  3. Partimento di Scienze e Tecnologie per LAgricoltura, le Foreste, la Natura e lEnergia, Università degli Studi della Tuscia, Via S. Camillo de Lellis, snc, 01100, Viterbo, Italy

    Maurizio Carlini

  4. Dipartimento di Scienze dell’Ingegneria Civile e dell’Architecttura, Politecnico di Bari, Via Orabona, 4, 70125, Bari, Italy

    Carmelo M. Torre

  5. International University VNU-HCM, Quarter 6, Linh Trung, Thu Duc, Ho Chi Minh City, Vietnam

    Hong-Quang Nguyen

  6. School of Business Systems, Monash University, 3800, Clayton, VIC, Australia

    David Taniar

  7. Department of Intelligent Informatics, Kyushu Sangyo University, 2-3-1 Matsukadai, Higashi-ku, 813-8503, Fukuoka, Japan

    Bernady O. Apduhan

  8. Department of Mathematics and Computer Science, University of Perugia, Via Vanvitelli, 1, 06123, Perugia, Italy

    Osvaldo Gervasi

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Aleb, N., Kechid, S. (2013). Automatic Test Data Generation Using a Genetic Algorithm. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2013. ICCSA 2013. Lecture Notes in Computer Science, vol 7972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39643-4_41

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  • DOI: https://doi.org/10.1007/978-3-642-39643-4_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39642-7

  • Online ISBN: 978-3-642-39643-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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