On the relative value of imbalanced learning for code smell detection
Machine learning‐based code smell detection (CSD) has been demonstrated to be a
valuable approach for improving software quality and enabling developers to identify …
valuable approach for improving software quality and enabling developers to identify …
A multi-objective effort-aware defect prediction approach based on NSGA-II
Abstract Effort-Aware Defect Prediction (EADP) technique sorts software modules by the
defect density and aims to find more bugs when testing a certain number of Lines of Code …
defect density and aims to find more bugs when testing a certain number of Lines of Code …
On the relative value of clustering techniques for unsupervised effort-aware defect prediction
P Yang, L Zhu, Y Zhang, C Ma, L Liu, X Yu… - Expert Systems with …, 2024 - Elsevier
Abstract Unsupervised Effort-Aware Defect Prediction (EADP) uses unlabeled data to
construct a model and ranks software modules according to the software feature values. Xu …
construct a model and ranks software modules according to the software feature values. Xu …
Data preparation for deep learning based code smell detection: A systematic literature review
Abstract Code Smell Detection (CSD) plays a crucial role in improving software quality and
maintainability. And Deep Learning (DL) techniques have emerged as a promising …
maintainability. And Deep Learning (DL) techniques have emerged as a promising …
Revisiting Code Smell Severity Prioritization using learning to rank techniques
Abstract Code Smell Severity Prioritization (CSSP) is crucial in hel** software developers
minimize software maintenance costs and enhance software quality, particularly when faced …
minimize software maintenance costs and enhance software quality, particularly when faced …
Improving the undersampling technique by optimizing the termination condition for software defect prediction
The class imbalance problem significantly hinders the ability of the software defect
prediction (SDP) models to distinguish between defective (minority class) and non-defective …
prediction (SDP) models to distinguish between defective (minority class) and non-defective …
Revisiting" code smell severity classification using machine learning techniques"
In the context of limited maintenance resources, predicting the severity of code smells is
more practically useful than simply detecting them. Fontana et al. first empirically …
more practically useful than simply detecting them. Fontana et al. first empirically …
Software defect prediction using learning to rank approach
Software defect prediction (SDP) plays a significant role in detecting the most likely defective
software modules and optimizing the allocation of testing resources. In practice, though …
software modules and optimizing the allocation of testing resources. In practice, though …
Multi-class Financial Distress Prediction Based on Feature Selection and Deep Forest Algorithm
X Chen, Z Mao, C Wu - Computational Economics, 2024 - Springer
The aim of this study is to develop an effective financial distress prediction (FDP) model that
enhances companies' understanding of their financial states. We propose a novel definition …
enhances companies' understanding of their financial states. We propose a novel definition …
IMDAC: A robust intelligent software defect prediction model via multi‐objective optimization and end‐to‐end hybrid deep learning networks
K Zhu, N Zhang, C Jiang, D Zhu - Software: Practice and …, 2024 - Wiley Online Library
Software defect prediction (SDP) aims to build an effective prediction model for historical
defect data from software repositories by some specialized techniques or algorithms, and …
defect data from software repositories by some specialized techniques or algorithms, and …