AUTOMATED TEST GENERATION TECHNIQUES FOR C++ SOFTWARE
DOI:
https://doi.org/10.26906/SUNZ.2025.2.102Keywords:
test automation, C++, regression testing, artificial bee colony, ant colony optimization, firefly algorithm, cuckoo search, q-learningAbstract
Automation of test script generation is critically important in modern software quality assurance, particularly for complex languages such as C++, where manual testing becomes resource-intensive and error-prone. Automated approaches significantly reduce the testing effort, improve test effectiveness, and enhance overall software reliability. The CIDER tool, introduced by the author, offers a promising automated solution by generating test scenarios based on recorded program executions using harmony search for inputs optimization. Despite its benefits, tool faces next limitations: incomplete coverage of complex branching logic, inefficient exploration of large input spaces, and difficulties in handling semantically rich or contextually complex input data. The overview article aims to systematically explore, compare, and evaluate various automated test generation techniques such as symbolic execution, concolic testing, evolutionary algorithms, reinforcement learning, and model-based testing. The objectives are: to classify these methods based on specified criteria and identify suitable approaches that could enhance the tool's capability for automated test generation in terms of coverage efficiency.Downloads
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Copyright (c) 2025 Mykhailo Hulevych, Oleksii Kolomiitsev
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