The characteristics of data is a open problem which has been tended perceived in data
analysis in machine learning research from last decades. The researcher defined some …

The performance of the learning models will intensely rely on the characteristics of the
training data. The previous outcomes recommend that the overlapping between classes and …

Machine learning techniques are frequent for the complicated task of predicting software
defects. Often the prediction models fail to predict defects successfully as the between class …

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 …

The main purpose of machine learning is to model the systems making predictions by using
some mathematical and operational features on the data with computers [1]. Today, there …

The dataset of software metrics in general are not balanced (unbalanced). An imbalance
distribution of classes and attributes that are not relevant may decrease the performance of …

As the size of software projects becomes larger, software defect prediction (SDP) will play a
key role in allocating testing resources reasonably, reducing testing costs, and speeding up …

Software defect prediction model is trained using code metrics and historical defect
information to identify probable software defects. The accuracy and performance of a …

Software defect prediction suffers from the class-imbalance. Solving the class-imbalance is
more important for improving the prediction performance. SMOTE is a useful over-sampling …

Different distance measures have been used for efficiently predicting software faults at early
stages of software development. One stereotyped approach for software fault prediction due …