A Systematic Literature Review on Explainability for Machine/Deep Learning-based Software Engineering Research

S Cao, X Sun, R Widyasari, D Lo, X Wu, L Bo… - arXiv preprint arXiv …, 2024 - arxiv.org
The remarkable achievements of Artificial Intelligence (AI) algorithms, particularly in
Machine Learning (ML) and Deep Learning (DL), have fueled their extensive deployment …

Evaluating the impact of data transformation techniques on the performance and interpretability of software defect prediction models

Y Zhao, Z Huang, L Gong, Y Zhu, Q Yu, Y Gao - IET Software, 2023 - Wiley Online Library
The performance of software defect prediction (SDP) models determines the priority of test
resource allocation. Researchers also use interpretability techniques to gain empirical …

LineFlowDP: A Deep Learning-Based Two-Phase Approach for Line-Level Defect Prediction

F Yang, F Zhong, G Zeng, P Xiao, W Zheng - Empirical Software …, 2024 - Springer
Software defect prediction plays a key role in guiding resource allocation for software
testing. However, previous defect prediction studies still have some limitations:(1) the …

Software Defect Prediction Using Deep Q‐Learning Network‐Based Feature Extraction

Q Zhang, J Zhang, T Feng, J Xue, X Zhu, N Zhu… - IET Software, 2024 - Wiley Online Library
Machine learning‐based software defect prediction (SDP) approaches have been
commonly proposed to help to deliver high‐quality software. Unfortunately, all the previous …

[引用][C] An Empirical Study on Model-Agnostic Techniques for Source Code-Based Defect Prediction

Y Zhu, Y Gao, Q Yu - International Journal of Software Engineering …, 2023 - World Scientific
Interpretation is important for adopting software defect prediction in practice. Model-agnostic
techniques such as Local Interpretable Model-agnostic Explanation (LIME) can help …