Finding the best learning to rank algorithms for effort-aware defect prediction
Abstract Context: Effort-Aware Defect Prediction (EADP) ranks software modules or changes
based on their predicted number of defects (ie, considering modules or changes as effort) or …
based on their predicted number of defects (ie, considering modules or changes as effort) or …
Software defect prediction with semantic and structural information of codes based on graph neural networks
C Zhou, P He, C Zeng, J Ma - Information and Software Technology, 2022 - Elsevier
Context: Most defect prediction methods consider a series of traditional manually designed
static code metrics. However, only using these hand-crafted features is impractical. Some …
static code metrics. However, only using these hand-crafted features is impractical. Some …
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 …
Revisiting 'revisiting supervised methods for effort‐aware cross‐project defect prediction'
Effort‐aware cross‐project defect prediction (EACPDP), which uses cross‐project software
modules to build a model to rank within‐project software modules based on the defect …
modules to build a model to rank within‐project software modules based on the defect …
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 …
Revisiting Code Smell Severity Prioritization using learning to rank techniques
Abstract Code Smell Severity Prioritization (CSSP) is crucial in helping software developers
minimize software maintenance costs and enhance software quality, particularly when faced …
minimize software maintenance costs and enhance software quality, particularly when faced …
Diverse title generation for Stack Overflow posts with multiple-sampling-enhanced transformer
Stack Overflow is one of the most popular programming communities where developers can
seek help for their encountered problems. Nevertheless, if inexperienced developers fail to …
seek help for their encountered problems. Nevertheless, if inexperienced developers fail to …
Detecting multi-type self-admitted technical debt with generative adversarial network-based neural networks
Context: Developers often introduce the self-admitted technical debt (SATD), ie, a
compromised solution to satisfy the delivery of the current goals, in code comments but do …
compromised solution to satisfy the delivery of the current goals, in code comments but do …
Exploring {ChatGPT's} Capabilities on Vulnerability Management
Recently, ChatGPT has attracted great attention from the code analysis domain. Prior works
show that ChatGPT has the capabilities of processing foundational code analysis tasks …
show that ChatGPT has the capabilities of processing foundational code analysis tasks …