Early detection of defective software components enables verification experts give much
time and allocate scare resources to the problem areas of the system under development …

Defect prediction can be useful to streamline testing efforts and reduce the development cost
of software. Predicting defects is usually done by using certain data mining and machine …

In this paper, we study the learning impact of data sampling followed by attribute selection
on the classification models built with binary class imbalanced data within the scenario of …

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

Software testing using software defect prediction aims to detect as many defects as possible
in software before the software release. This plays an important role in ensuring quality and …

Two primary issues have emerged in the machine learning and data mining community: how
to deal with imbalanced data and how to choose appropriate features. These are of …

Naïve Bayes is one of the most simplest and efficient classification algorithms and is
therefore being widely used. It has been utilized in several situations, like web mining …

Software Defect Prediction (SDP) proposes to define the exposure of software to defect by
building prediction models through using defect data and the software metrics with several …

Software defect prediction strives to improve software quality and testing efficiency by
constructing predictive classification models from code attributes to enable a timely …

Software defect prediction is the process of locating defective modules in software. It
facilitates testing efficiency and consequently software quality. It enables a timely …