Software defect prediction, aimed at assisting software practitioners in allocating test
resources more efficiently, predicts the potential defective modules in software products …

Abstract Effort‐Aware Defect Prediction (EADP) methods sort software modules based on
the defect density and guide the testing team to inspect the modules with high defect density …

Cross-project defect prediction (CPDP) is an active research topic in the domain of software
defect prediction, since CPDP can be applied to the following scenarios: the target project …

Cross-project defect prediction (CPDP) methods can be used when the target project is a
new project or lacks enough labeled program modules. In these new target projects, we can …

As the boom of mobile devices, Android mobile apps play an irreplaceable roles in people's
daily life, which have the characteristics of frequent updates involving in many code commits …

Mutation-based fault localization (MBFL) is a recently proposed technique with the
advantage of high fault localization accuracy. However, such a mutation analysis based …

Data available in software engineering for many applications contains variability and it is not
possible to say which variable helps in the process of the prediction. Most of the work …

Context: Cross-project defect prediction applies to the scenarios that the target projects are
new projects. Most of the previous studies tried to utilize the training data from other projects …

Heterogeneous cross-project defect prediction (HCPDP) aims to learn a prediction model
from a heterogeneous source project and then apply the model to a target project. Existing …

Security vulnerability prediction (SVP) can construct models to identify potentially vulnerable
program modules via machine learning. Two kinds of features from different points of view …