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A systematic survey of just-in-time software defect prediction
Recent years have experienced sustained focus in research on software defect prediction
that aims to predict the likelihood of software defects. Moreover, with the increased interest …
that aims to predict the likelihood of software defects. Moreover, with the increased interest …
[HTML][HTML] On the use of deep learning in software defect prediction
Context: Automated software defect prediction (SDP) methods are increasingly applied,
often with the use of machine learning (ML) techniques. Yet, the existing ML-based …
often with the use of machine learning (ML) techniques. Yet, the existing ML-based …
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
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 …
The impact of feature selection techniques on effort‐aware defect prediction: An empirical study
Abstract Effort‐Aware Defect Prediction (EADP) methods sort software modules based on
the defect density and guide the testing team to inspect the modules with high defect density …
the defect density and guide the testing team to inspect the modules with high defect density …
On the relative value of clustering techniques for Unsupervised Effort-Aware Defect Prediction
Abstract Unsupervised Effort-Aware Defect P rediction (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 …
bjCnet: A contrastive learning-based framework for software defect prediction
Defect prediction based on deep learning is proposed to provide practitioners with reliable
and practical tools to determine whether an area of code is defective. Compared with …
and practical tools to determine whether an area of code is defective. Compared with …
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 …
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 …
Multi‐task deep neural networks for just‐in‐time software defect prediction on mobile apps
With the development of smartphones, mobile applications play an irreplaceable role in our
daily life, which characteristics often commit code changes to meet new requirements. This …
daily life, which characteristics often commit code changes to meet new requirements. This …