A hybrid instance selection using nearest-neighbor for cross-project defect prediction
Software defect prediction (SDP) is an active research field in software engineering to
identify defect-prone modules. Thanks to SDP, limited testing resources can be effectively …
identify defect-prone modules. Thanks to SDP, limited testing resources can be effectively …
Towards building a universal defect prediction model with rank transformed predictors
Software defects can lead to undesired results. Correcting defects costs 50% to 75% of the
total software development budgets. To predict defective files, a prediction model must be …
total software development budgets. To predict defective files, a prediction model must be …
Deep learning for software defect prediction: A survey
S Omri, C Sinz - Proceedings of the IEEE/ACM 42nd international …, 2020 - dl.acm.org
Software fault prediction is an important and beneficial practice for improving software
quality and reliability. The ability to predict which components in a large software system are …
quality and reliability. The ability to predict which components in a large software system are …
Best neighbor-guided artificial bee colony algorithm for continuous optimization problems
H Peng, C Deng, Z Wu - Soft computing, 2019 - Springer
As a relatively recent invented swarm intelligence algorithm, artificial bee colony (ABC)
becomes popular and is powerful for solving the tough continuous optimization problems …
becomes popular and is powerful for solving the tough continuous optimization problems …
Tackling class imbalance problem in software defect prediction through cluster-based over-sampling with filtering
L Gong, S Jiang, L Jiang - IEEE Access, 2019 - ieeexplore.ieee.org
In practice, Software Defect Prediction (SDP) models often suffer from highly imbalanced
data, which makes classifiers difficult to identify defective instances. Recently, many …
data, which makes classifiers difficult to identify defective instances. Recently, many …
A comprehensive comparative study of clustering-based unsupervised defect prediction models
Software defect prediction recommends the most defect-prone software modules for
optimization of the test resource allocation. The limitation of the extensively-studied …
optimization of the test resource allocation. The limitation of the extensively-studied …
File-level defect prediction: Unsupervised vs. supervised models
Background: Software defect models can help software quality assurance teams to allocate
testing or code review resources. A variety of techniques have been used to build defect …
testing or code review resources. A variety of techniques have been used to build defect …
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 …
The use of summation to aggregate software metrics hinders the performance of defect prediction models
Defect prediction models help software organizations to anticipate where defects will appear
in the future. When training a defect prediction model, historical defect data is often mined …
in the future. When training a defect prediction model, historical defect data is often mined …
[PDF][PDF] 静态软件缺陷预测方法研究
陈翔， 顾庆， 刘望舒， 刘树龙， 倪超 - 软件学报, 2015 - jos.org.cn
静态软件缺陷预测是软件工程数据挖掘领域中的一个研究热点. 通过分析软件代码或开发过程,
设计出与软件缺陷相关的度量元; 随后, 通过挖掘软件历史仓库来创建缺陷预测数据集 …
设计出与软件缺陷相关的度量元; 随后, 通过挖掘软件历史仓库来创建缺陷预测数据集 …