Automated Test Case Generation from Input Specification in Natural Language

Li, Tianyu; Lu, Xiuwen; Xu, Hui

doi:10.1109/issrew55968.2022.00076

Cited by 2 publications

(1 citation statement)

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“…It uses static analysis of existing tests for mining of literals, which can be suitable as input values in generated tests in a specific domain. Yet another different approach is described in [63]. There, the test cases are generated from inputs specification in natural language.…”

Section: E Data-description-based Methodsmentioning

confidence: 99%

Current Trends in Automated Test Case Generation

Potuzak,

Lipka

2023

Annals of Computer Science and Information Systems

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The testing is an integral part of the software development. At the same time, the manual creation of individu-al test cases is a lengthy and error-prone process. Hence, an intensive research on automated test generation methods is ongoing for more than twenty years. There are many vastly dif-ferent approaches, which can be considered automated test case generation. However, a common feature is the generation of the data for the test cases. Ultimately, the test data decide the prog-ram branching and can be used on any testing level, starting with the unit tests and ending with the tests focused on the behavior of the entire application. The test data are also mostly independent on any specific technology, such as programming language or paradigm. This paper is a survey of existing litera-ture of the last two decades that deals with test data generation or with tests based on it. This survey is not a systematic literature review and it does not try to answer specific scientific questions formulated in advance. Its purpose is to map and categorize the existing methods and to summarize their common features. Such a survey can be helpful for any teams developing their methods for test data generation as it can be a starting point for the exploration of related work.Index terms-Software testing, test case generation, test data generation, papers survey.

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Section: E Data-description-based Methodsmentioning

confidence: 99%