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Journal of Information and Optimization Sciences cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Open Access Research Article

Recent trends on data flow testing : A review

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pp. 1189–1197Vol. 45Issue 4May 2024DOI: 10.47974/JIOS-1702XML
Published Online:
08 Jun 2024
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1702
Pages:
1189–1197

Abstract

The research aims to comprehensively examine the current state of knowledge in data flow testing (DFT) to identify knowledge gaps and inform future research. The authors undertook this research question to advance the practice of DFT and improve software systems’ quality and reliability. The authors analyze several state-of-the-art techniques, including the correlation tree concept and particle swarm optimization algorithm, leveraging the data flow knowledge during test execution, as well as neural networks and genetic algorithms. The authors also discuss the methods used to evaluate the effectiveness and accuracy of the various techniques, including case studies, simulations, and test data-generating techniques. The authors aim to provide a superficial understanding of the field’s current state and note that future work could focus on in-depth analysis of specific areas within DFT. Future researchers can use this research to gain a deeper understanding of current algorithms and work on improving them. This is particularly important as DFT is an essential part of the testing process for any software.

Keywords

Subject Classifications

68U99

References

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