A comparative study to benchmark cross-project defect prediction approaches
Cross-Project Defect Prediction (CPDP) as a means to focus quality assurance of software
projects was under heavy investigation in recent years. However, within the current state-of …
projects was under heavy investigation in recent years. However, within the current state-of …
TLEL: A two-layer ensemble learning approach for just-in-time defect prediction
Context Defect prediction is a very meaningful topic, particularly at change-level. Change-
level defect prediction, which is also referred as just-in-time defect prediction, could not only …
level defect prediction, which is also referred as just-in-time defect prediction, could not only …
Cross-project defect prediction using a connectivity-based unsupervised classifier
Defect prediction on projects with limited historical data has attracted great interest from both
researchers and practitioners. Cross-project defect prediction has been the main area of …
researchers and practitioners. Cross-project defect prediction has been the main area of …
An empirical study of model-agnostic techniques for defect prediction models
J Jiarpakdee, CK Tantithamthavorn… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
Software analytics have empowered software organisations to support a wide range of
improved decision-making and policy-making. However, such predictions made by software …
improved decision-making and policy-making. However, such predictions made by software …
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 …
An empirical study on software defect prediction with a simplified metric set
Context Software defect prediction plays a crucial role in estimating the most defect-prone
components of software, and a large number of studies have pursued improving prediction …
components of software, and a large number of studies have pursued improving prediction …
Automatic feature learning for predicting vulnerable software components
Code flaws or vulnerabilities are prevalent in software systems and can potentially cause a
variety of problems including deadlock, hacking, information loss and system failure. A …
variety of problems including deadlock, hacking, information loss and system failure. A …
How far we have progressed in the journey? an examination of cross-project defect prediction
Background. Recent years have seen an increasing interest in cross-project defect
prediction (CPDP), which aims to apply defect prediction models built on source projects to a …
prediction (CPDP), which aims to apply defect prediction models built on source projects to a …
Software defect prediction: do different classifiers find the same defects?
During the last 10 years, hundreds of different defect prediction models have been
published. The performance of the classifiers used in these models is reported to be similar …
published. The performance of the classifiers used in these models is reported to be similar …
Bug localization with combination of deep learning and information retrieval
The automated task of locating the potential buggy files in a software project given a bug
report is called bug localization. Bug localization helps developers focus on crucial files …
report is called bug localization. Bug localization helps developers focus on crucial files …