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Hybrid test data generation

Published: 31 May 2014 Publication History

Abstract

Many automatic test data generation techniques have been proposed in the past decades. Each technique can only deal with very restrictive data types so far. This limits the usefulness of test data generation in practice. We present a preliminary approach on hybrid test data generation, by combining Random Strategy (RS), Dynamic Symbolic Execution (DSE), and Search-based Strategy (SBS). It is expected to take advantage of the state-of-the-arts to enhance the robustness and scalability, in terms of different types of test data.

References

[1]
RS Random Cost-effective Unstable ∗
[2]
DSE Constraints Structural Path explosion, int, enums
[3]
{8} of paths information complex constraints SBS Objective Scalability Local optima, float, string
[4]
M. Alshraideh and L. Bottaci. Search-based software test data generation for string data using program-specific search operators. STVR, 16(3):175–203, 2006.
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B. Botella, A. Gotlieb, and C. Michel. Symbolic execution of floating-point computations. STVR, 16(2):97–121, 2006.
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P. Godefroid, N. Klarlund, and K. Sen. Dart: directed automated random testing. ACM SIGPLAN Notices, 40:213–223, 2005.
[7]
M. Harman and P. McMinn. A theoretical and empirical study of search-based testing: Local, global, and hybrid search. IEEE TSE, 36(2):226–247, 2010.
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K. Lakhotia, N. Tillmann, M. Harman, and J. De Halleux. Flopsy-search-based floating point constraint solving for symbolic execution. Testing Software and Systems, pages 142–157, 2010.
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R. Majumdar and K. Sen. Hybrid concolic testing. ICSE, pp. 416–426, 2007.
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J. Malburg and G. Fraser. Combining search-based and constraint-based testing. ASE, pp. 436–439, 2011.
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K. Sen, D. Marinov, and G. Agha. CUTE: a concolic unit testing engine for C, ESEC/FSE, pp. 263–272, 2005.

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  • (2022)Automatic Test Data Generation Using Genetic Algorithm for Python Programs2022 2nd International Conference on Computing and Information Technology (ICCIT)10.1109/ICCIT52419.2022.9711607(197-205)Online publication date: 25-Jan-2022
  • (2022)Implementation of the Test Data Generation Algorithm Based on the Ant Colony Optimization Pheromone ModelAdvances in Swarm Intelligence10.1007/978-3-031-09677-8_21(247-258)Online publication date: 26-Jun-2022
  • (2021)Automated Test Data Generation Based on a Genetic Algorithm with Maximum Code Coverage and Population DiversityApplied Sciences10.3390/app1110467311:10(4673)Online publication date: 20-May-2021
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cover image ACM Conferences
ICSE Companion 2014: Companion Proceedings of the 36th International Conference on Software Engineering
May 2014
741 pages
ISBN:9781450327688
DOI:10.1145/2591062
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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  • TCSE: IEEE Computer Society's Tech. Council on Software Engin.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 May 2014

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Author Tags

  1. Hybrid strategy
  2. dynamic symbolic execution
  3. random strategy
  4. search-based strategy
  5. test data generation

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Overall Acceptance Rate 276 of 1,856 submissions, 15%

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Cited By

View all
  • (2022)Automatic Test Data Generation Using Genetic Algorithm for Python Programs2022 2nd International Conference on Computing and Information Technology (ICCIT)10.1109/ICCIT52419.2022.9711607(197-205)Online publication date: 25-Jan-2022
  • (2022)Implementation of the Test Data Generation Algorithm Based on the Ant Colony Optimization Pheromone ModelAdvances in Swarm Intelligence10.1007/978-3-031-09677-8_21(247-258)Online publication date: 26-Jun-2022
  • (2021)Automated Test Data Generation Based on a Genetic Algorithm with Maximum Code Coverage and Population DiversityApplied Sciences10.3390/app1110467311:10(4673)Online publication date: 20-May-2021
  • (2021)Development and Research of the Test Data Generation Approach Modifications2021 International Conference on Information Technology and Nanotechnology (ITNT)10.1109/ITNT52450.2021.9649110(1-6)Online publication date: 20-Sep-2021
  • (2021)Formulation and research of new fitness function in the genetic algorithm for maximum code coverageProcedia Computer Science10.1016/j.procs.2021.04.194186(713-720)Online publication date: 2021
  • (2021)Genetic Algorithm Fitness Function Formulation for Test Data Generation with Maximum Statement CoverageAdvances in Swarm Intelligence10.1007/978-3-030-78743-1_34(379-389)Online publication date: 7-Jul-2021
  • (2019)Using genetic algorithm for generating optimal data sets to automatic testing the program codeInformation Technology and Nanotechnology10.18287/1613-0073-2019-2416-173-182(173-182)Online publication date: 2019
  • (2018)Hybrid statistical estimation of mutual information and its application to information flowFormal Aspects of Computing10.1007/s00165-018-0469-zOnline publication date: 8-Oct-2018

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