Big data is an emerging trend of information technology. Large amount of data are growing very fast these days. To handle this huge amount of data, we need a technology which can extract the complex representation of data. Several approaches exist nowadays; one of the latest paradigm is deep learning. To excerpt difficult representation automatically, deep learning algorithms are used widely for unsupervised data. To resolve an extremely complex problem we used deep learning algorithm which is inspired by field of Artificial intelligence, whose goal is match human brain's potential to perceive, evaluate, grasp and decision making. The real motivation of the deep learning algorithm is to handle complex problem effectively. The unsupervised data representation is directly extracted without the participation of human. The main goal of deep learning is to make a machine which is independent of human intervention. It generates some patterns and their relationship, the algorithms perform an effective task based on that data.

The number of issues fixed in the current release of the software is one of the factors which decides the next release of the software. The source code files get changed during fixing of these issues. The uncertainty arises due to these changes is quantified using entropy based measures. We developed a Non-Homogeneous Poisson Process model for Open Source Software to understand the fixing of issues across releases. Based on this model, optimal release-updating using entropy and maximizing the active user's satisfaction level subject to fixing of issues up to a desired level, is investigated as well. The proposed models have been validated on five products of the Apache open source project. The optimal release time estimated from the proposed model is close to the observed release time at different active user's satisfaction levels. The proposed decision model can assist management to appropriately determine the optimal release-update time. The proposed entropy based model for issues estimation shows improvement in performance for 21 releases out of total 23 releases, when compared with well-known traditional software reliability growth models, namely GO model[1]and S-shaped model[2]. The proposed model is also found statistically significant.

In bug repositories, we receive a large number of bug reports on daily basis. Managing such a large repository is a challenging job. Priority of a bug tells that how important and urgent it is for us to fix. Priority of a bug can be classified into 5 levels from PI to P5 where PI is the highest and P5 is the lowest priority. Correct prioritization of bugs helps in bug fix scheduling/assignment and resource allocation. Failure of this will result in delay of resolving important bugs. This requires a bug prediction system which can predict the priority of a newly reported bug. Cross project validation is also an important concern in empirical software engineering where we train classifier on one project and test it for prediction on other projects. In the available literature, we found very few papers for bug priority prediction and none of them dealt with cross project validation. In this paper, we have evaluated the performance of different machine learning techniques namely Support Vector Machine (SVM), Naive Bayes (NB), K-Nearest Neighbors (KNN) and Neural Network (NNet) in predicting the priority of the newly coming reports on the basis of different performance measures. We performed cross project validation for 76 cases of five data sets of open office and eclipse projects. The accuracy of different machine learning techniques in predicting the priority of a reported bug within and across project is found above 70% except Naive Bayes technique.