  In recent years, defect prediction, one of the major software engineering
problems, has been in the focus of researchers since it has a pivotal role in
estimating software errors and faulty modules. Researchers with the goal of
improving prediction accuracy have developed many models for software defect
prediction. However, there are a number of critical conditions and theoretical
problems in order to achieve better results. In this paper, two deep learning
models, Stack Sparse Auto-Encoder (SSAE) and Deep Belief Network (DBN), are
deployed to classify NASA datasets, which are unbalanced and have insufficient
samples. According to the conducted experiment, the accuracy for the datasets
with sufficient samples is enhanced and beside this SSAE model gains better
results in comparison to DBN model in the majority of evaluation metrics.
