A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Integrating convolutional neural networks for improved software engineering: A Collaborative and unbalanced data Perspective
This study pioneers the tailored application of Convolutional Neural Networks (CNNs) for addressing the challenge of unbalanced data in software engineering, a relatively unexplored domain for CNN utilization. Unlike conventional methods, our framework demonstrates a significant precision uplift of up to 15% in software classification tasks, specifically enhancing minority class sample accuracy. This research not only delineates a novel CNN-based approach that outperforms traditional data balancing techniques but also underscores the strategic integration of AI to bolster software engineering processes. By pinpointing the ethical implications, our findings advocate for a conscientious adoption of AI, ensuring software development advances equitably and efficiently.
Integrating convolutional neural networks for improved software engineering: A Collaborative and unbalanced data Perspective
This study pioneers the tailored application of Convolutional Neural Networks (CNNs) for addressing the challenge of unbalanced data in software engineering, a relatively unexplored domain for CNN utilization. Unlike conventional methods, our framework demonstrates a significant precision uplift of up to 15% in software classification tasks, specifically enhancing minority class sample accuracy. This research not only delineates a novel CNN-based approach that outperforms traditional data balancing techniques but also underscores the strategic integration of AI to bolster software engineering processes. By pinpointing the ethical implications, our findings advocate for a conscientious adoption of AI, ensuring software development advances equitably and efficiently.
Integrating convolutional neural networks for improved software engineering: A Collaborative and unbalanced data Perspective
Mohammadreza Nehzati (author)
2024
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
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