Background: Ensemble techniques have gained attention in various scientific fields. Defect
prediction researchers have investigated many state-of-the-art ensemble models and …

Accurately predicting faulty software units helps practitioners target faulty units and prioritize
their efforts to maintain software quality. Prior studies use machine-learning models to detect …

Background Unsupervised machine learners have been increasingly applied to software
defect prediction. It is an approach that may be valuable for software practitioners because it …

Software defect prediction is the process of identifying defective files and modules that need
rigorous testing. In the literature, several secondary studies including systematic reviews …

Software defect prediction is one of the emerging research areas of software engineering.
The prediction of defects at early stage of development process can produce high quality …

Research has yielded approaches to predict future defects in software artifacts based on
historical information, thus assisting companies in effectively allocating limited development …

Two important problems which can affect the performance of classification models are high-
dimensionality (an overabundance of independent features in the dataset) and imbalanced …

Just-in-time software defect prediction (JIT-SDP) is a fine-grained, easy-to-trace, and
practical method. Unfortunately, JIT-SDP usually suffers from the class imbalance problem …

Software defect prediction is a popular technical method in software engineering. In order to
reduce the cost of a software defects, problems existing in the software are found by testing …

Prediction of software faults using different machine learning techniques has been reported
earlier by many researchers. However, these individual techniques suffered from …