Interpretable Deep Learning for Efficient Code Smell Prioritization in Software Development

Interpretable Deep Learning for Efficient Code Smell Prioritization in Software Development

Code smells indicate potential design flaws in software systems that can impair maintainability and increase technical debt. While existing approaches have advanced code smell priortization, they often lack effective prioritization mechanisms and interpretability, hindering developers’ ab...

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Bibliographic Details
Main Authors: Maaeda M. Rashid, Mohd Hafeez Osman, Khaironi Yatim Sharif, Hazura Zulzalil
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Bidirectional long short-term memory
code smells
CodeBERT
deep learning
feedforward neural networks
local interpretable model-agnostic explanations
Online Access:https://ieeexplore.ieee.org/document/10907894/
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https://ieeexplore.ieee.org/document/10907894/

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