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Open Access

Output Type Guided Random Test Case Generation for String Validation Routines

School of Computer Science and Engineering, Macau University of Science and Technology, Macau 999078, China
School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China
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Abstract

String validation routines have been widely used in many real-world applications, such as email validation and postcode validation. String test cases are adopted to test these validation routines, to identify potential defects and security risks. Random Testing (RT) is a well-known testing approach to randomly generate string test cases from the input domain (i.e., the set of all possible test inputs), which is simple to implement at a low cost. However, its testing effectiveness may be unsatisfactory for string validation routines. The main reason for this is that RT may have a high probability to generate invalid rather than valid string test cases, due to its randomness property. This research proposes a new RT approach based on the output types (i.e., valid and invalid strings) for string validation routines, namely Output-type-guided Random Testing (RT-O), which attempts to randomly generate both valid and invalid string test cases with a certain probability. This research performed an empirical study involving several real-world string validation routines collected from ten Java open-source projects, to investigate and compare testing performances of RT-O against the previous two widely-used RT methods. The results show that the generated string test cases by RT-O outperform test cases generated by other RT methods.

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Tsinghua Science and Technology
Pages 2467-2486

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Cite this article:
Cui C, Huang R, Chen J, et al. Output Type Guided Random Test Case Generation for String Validation Routines. Tsinghua Science and Technology, 2025, 30(6): 2467-2486. https://doi.org/10.26599/TST.2024.9010023

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Received: 06 November 2023
Revised: 04 January 2024
Accepted: 25 January 2024
Published: 04 July 2025
© The author(s) 2025.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).