Predicting the defect-proneness of a module can reduce the time, effort, manpower, and
consequently the cost to develop a software project. Since the causes of software defects …

Testing is considered as one of the expensive activities in software development process.
Fixing the defects during testing process can increase the cost as well as the completion …

Software defect prediction (SDP) technology is receiving widely attention and most of SDP
models are trained on data from the same project. However, at an early phase of the …

Abstract Machine learning methods have been applied in software engineering to effectively
predict software defects. Researchers proposed cross-project defect prediction (CPDP) for …

Software defect prediction plays a crucial role in enhancing software quality while achieving
cost savings in testing. Its primary objective is to identify and send only defective modules to …

Using classification methods to predict software defect is receiving a great deal of attention
and most of the existing studies primarily conduct prediction under the within‐project setting …

软件缺陷预测方法可以在项目的开发初期, 通过预先识别出所有可能含有缺陷的软件模块来优化
测试资源的分配. 早期的缺陷预测研究大多集中于同项目缺陷预测, 但同项目缺陷预测需要充足 …

Context Cross-project defect prediction (CPDP) which uses dataset from other projects to
build predictors has been recently recommended as an effective approach for building …

Software defect prediction (SDP) has been a very important research topic in software
engineering, since it can provide high‐quality results when given sufficient historical data of …

Software defect prediction is used to identify modules in software projects that may have
defects. Heterogeneous Defect Prediction (HDP) establishes a cross project defect …