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 …
Survey of software defect prediction features
S Qiu, BE, J He, L Liu - Neural Computing and Applications, 2024 - Springer
Software defect prediction (SDP) is a technique that uses known software features and
defect information to predict target software defects. It helps reduce software development …
defect information to predict target software defects. It helps reduce software development …
Improving effort-aware defect prediction by directly learning to rank software modules
Abstract Context: Effort-Aware Defect Prediction (EADP) ranks software modules according
to the defect density of software modules, which allows testers to find more bugs while …
to the defect density of software modules, which allows testers to find more bugs while …
Enhancing Software Defect Prediction Through Root Cause Analysis: A Hybrid Approach Integrating Permutation Importance with XGBoost (PERMBoost)
M Mustaqeem, S Mustajab… - … , Computer Sciences and …, 2024 - ieeexplore.ieee.org
Software Defect Prediction (SDP) plays an essential role in ensuring software quality and
minimizing the costs associated with software failures. Conventional defect prediction …
minimizing the costs associated with software failures. Conventional defect prediction …
[PDF][PDF] SOFTWARE RELIABILITY ANALYSIS BY USING THE BIDIRECTIONAL ATTENTION BASED ZEILER-FERGUS CONVOLUTIONAL NEURAL NETWORK.
D Sudharson, R Gomathi, L Selvam - Neural Network World, 2024 - nnw.cz
Software quality assurance relies heavily on software reliability as one of its primary metrics.
Numerous studies have been conducted to identify the software reliability. Improved …
Numerous studies have been conducted to identify the software reliability. Improved …
An Evaluation of Effort-Aware Fine-Grained Just-in-Time Defect Prediction Methods
S Amasaki, H Aman, T Yokogawa - 2022 48th Euromicro …, 2022 - ieeexplore.ieee.org
CONTEXT: Software defect prediction (SDP) is an active research topic to support software
quality assurance (SQA) activities. It was observed that unsupervised prediction models …
quality assurance (SQA) activities. It was observed that unsupervised prediction models …
The Impact of the bug number on Effort-Aware Defect Prediction: An Empirical Study
P Yang, L Zhu, W Hu, JW Keung, L Lu… - Proceedings of the 14th …, 2023 - dl.acm.org
Previous research have utilized public software defect datasets such as NASA, RELINK, and
SOFTLAB, which only contain class label information. Almost all Effort-Aware Defect …
SOFTLAB, which only contain class label information. Almost all Effort-Aware Defect …
Aggregation as Unsupervised Learning in Software Engineering and Beyond
M Ulan - 2021 - diva-portal.org
Engineering and Beyond, Linnaeus University Dissertations No 430/2021, ISBN: 978-91-
89460-40-9 (print), 978-91-89460-41-6 (pdf). Ranking alternatives is fundamental to …
89460-40-9 (print), 978-91-89460-41-6 (pdf). Ranking alternatives is fundamental to …
Aggregation as Unsupervised Learning and its Evaluation
Regression uses supervised machine learning to find a model that combines several
independent variables to predict a dependent variable based on ground truth (labeled) data …
independent variables to predict a dependent variable based on ground truth (labeled) data …
[HTML][HTML] 几类度量聚合函数的研究
孙利军， 赵晨， 李钢 - 2023 - xml-data.org
几类度量聚合函数的研究 齐鲁工业大学学报 2023, Vol. 37 Issue (5): 60-66 0 引用本文 孙利军,
赵晨, 李钢. 几类度量聚合函数的研究[J]. 齐鲁工业大学学报, 2023, 37(5): 60-66. DOI: 10.16442/j.cnki.qlgydxxb.2023.05.008 …
赵晨, 李钢. 几类度量聚合函数的研究[J]. 齐鲁工业大学学报, 2023, 37(5): 60-66. DOI: 10.16442/j.cnki.qlgydxxb.2023.05.008 …