Understanding Flaky Tests Through Linguistic Diversity: A Cross-Language and Comparative Machine Learning Study

Software development is significantly impeded by flaky tests, which intermittently pass or fail without requiring code modifications, resulting in a decline in confidence in automated testing frameworks. Code smells (i.e., test case or production code) are the primary cause of test flakiness. In ord...

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Vydané v:	IEEE access Ročník 13; s. 54561 - 54584
Hlavní autori:	Ahmad, Azeem, Sun, Xin, Naeem, Muhammad Rashid, Javed, Yasir, Akour, Mohammad, Sandahl, Kristian
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Accuracy artificial intelligence Automation Codes Copyright Datasets Decision making Decision trees Discrepancies Effectiveness Ensemble learning Feature extraction Flaky tests Generalizability Java Language Language acquisition Language diversity Language tests Language varieties Languages Linguistics Machine learning Mitigation non-deterministic tests Programming Programming languages Python Quantitative analysis Random forests Regression analysis Reliability Reproducibility Reproducibility of results Software Software development software testing Source code Statistical analysis Support vector machines Test validity and reliability Tests Training Vocabulary Vocabulary Java Codes software testing Flaky tests Programming Reproducibility of results machine learning Random forests artificial intelligence Training Accuracy Feature extraction non-deterministic tests Python
ISSN:	2169-3536, 2169-3536
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