  The increased computerization in recent years has resulted in the production
of a variety of different software, however measures need to be taken to ensure
that the produced software isn't defective. Many researchers have worked in
this area and have developed different Machine Learning-based approaches that
predict whether the software is defective or not. This issue can't be resolved
simply by using different conventional classifiers because the dataset is
highly imbalanced i.e the number of defective samples detected is extremely
less as compared to the number of non-defective samples. Therefore, to address
this issue, certain sophisticated methods are required. The different methods
developed by the researchers can be broadly classified into Resampling based
methods, Cost-sensitive learning-based methods, and Ensemble Learning. Among
these methods. This report analyses the performance of the Online Sequential
Extreme Learning Machine (OS-ELM) proposed by Liang et.al. against several
classifiers such as Logistic Regression, Support Vector Machine, Random Forest,
and Na\"ive Bayes after oversampling the data. OS-ELM trains faster than
conventional deep neural networks and it always converges to the globally
optimal solution. A comparison is performed on the original dataset as well as
the over-sampled data set. The oversampling technique used is Cluster-based
Over-Sampling with Noise Filtering. This technique is better than several
state-of-the-art techniques for oversampling. The analysis is carried out on 3
projects KC1, PC4 and PC3 carried out by the NASA group. The metrics used for
measurement are recall and balanced accuracy. The results are higher for OS-ELM
as compared to other classifiers in both scenarios.
