Progress on approaches to software defect prediction
Software defect prediction is one of the most popular research topics in software
engineering. It aims to predict defect‐prone software modules before defects are discovered …
engineering. It aims to predict defect‐prone software modules before defects are discovered …
Predicting the precise number of software defects: Are we there yet?
Abstract Context: Defect Number Prediction (DNP) models can offer more benefits than
classification-based defect prediction. Recently, many researchers proposed to employ …
classification-based defect prediction. Recently, many researchers proposed to employ …
The impact of automated parameter optimization on defect prediction models
C Tantithamthavorn, S McIntosh… - IEEE Transactions …, 2018 - ieeexplore.ieee.org
Defect prediction models-classifiers that identify defect-prone software modules-have
configurable parameters that control their characteristics (eg, the number of trees in a …
configurable parameters that control their characteristics (eg, the number of trees in a …
Software defect prediction based on kernel PCA and weighted extreme learning machine
Context Software defect prediction strives to detect defect-prone software modules by mining
the historical data. Effective prediction enables reasonable testing resource allocation …
the historical data. Effective prediction enables reasonable testing resource allocation …
Software defect prediction based on enhanced metaheuristic feature selection optimization and a hybrid deep neural network
K Zhu, S Ying, N Zhang, D Zhu - Journal of Systems and Software, 2021 - Elsevier
Software defect prediction aims to identify the potential defects of new software modules in
advance by constructing an effective prediction model. However, the model performance is …
advance by constructing an effective prediction model. However, the model performance is …
Performance analysis of feature selection methods in software defect prediction: a search method approach
Software Defect Prediction (SDP) models are built using software metrics derived from
software systems. The quality of SDP models depends largely on the quality of software …
software systems. The quality of SDP models depends largely on the quality of software …
Comparing heuristic and machine learning approaches for metric-based code smell detection
Code smells represent poor implementation choices performed by developers when
enhancing source code. Their negative impact on source code maintainability and …
enhancing source code. Their negative impact on source code maintainability and …
Improving defect prediction with deep forest
Context Software defect prediction is important to ensure the quality of software. Nowadays,
many supervised learning techniques have been applied to identify defective instances (eg …
many supervised learning techniques have been applied to identify defective instances (eg …
The impact of feature reduction techniques on defect prediction models
Defect prediction is an important task for preserving software quality. Most prior work on
defect prediction uses software features, such as the number of lines of code, to predict …
defect prediction uses software features, such as the number of lines of code, to predict …
A large-scale study of the impact of feature selection techniques on defect classification models
The performance of a defect classification model depends on the features that are used to
train it. Feature redundancy, correlation, and irrelevance can hinder the performance of a …
train it. Feature redundancy, correlation, and irrelevance can hinder the performance of a …