The accurate severity classification of a bug report is an important aspect of bug fixing. The bug reports are submitted into the bug tracking system with high speed, and owing to this, bug repository size has been increasing at an enormous rate. This increased bug repository size introduces biases in the bug triage process. Therefore, it is necessary to classify the severity of a bug report to balance the bug triaging process. Previously, many machine learning models were proposed for automation of bug severity classification. The accuracy of these models is not up to the mark because they do not extract the important feature patterns for learning the classifier. This paper proposes a novel deep learning model for multiclass severity classification called Bug Severity classification to address these challenges by using a Convolutional Neural Network and Random forest with Boosting (BCR). This model directly learns the latent and highly representative features. Initially, the natural language techniques preprocess the bug report text, and then n-gram is used to extract the features. Further, the Convolutional Neural Network extracts the important feature patterns of respective severity classes. Lastly, the random forest with boosting classifies the multiple bug severity classes. The average accuracy of the proposed model is 96.34% on multiclass severity of five open source projects. The average F-measures of the proposed BCR and the existing approach were 96.43% and 84.24%, respectively, on binary class severity classification. The results prove that the proposed BCR approach enhances the performance of bug severity classification over the state-of-the-art techniques.

Duplicate bug report detection is a process of finding a duplicate bug report in the bug tracking system. This process is essential to avoid unnecessary work and rediscovery. In typical bug tracking systems, more than thousands of duplicate bug reports are reported every day. In turn, human cost, effort and time are increased. This makes it an important problem in the software management process. The solution is to automate the duplicate bug report detection system for reducing the manual effort, thus the productivity of triager's and developer's is increased. It also speeds up the process of software management as a result software maintenance cost is also reduced. However, existing systems are not quite accurate yet, in spite of these systems used various machine learning approaches. In this work, an automatic bug report detection and classification model is proposed using deep learning technique. The proposed system has three modules i.e. Preprocessing, Deep Learning Model and Duplicate Bug report Detection and Classification. Further, the proposed model used Convolutional Neural Network based deep learning model to extract relevant feature. These relevant features are used to determine the similar features of bug reports. Hence, the bug reports similarity is computers through these similar features. The performance of the proposed system is evaluated on six publicly available datasets using six performance metrics. It is noticed that the proposed system outperforms the existing systems by achieving an accuracy rate in the range of 85% to 99 % and recall@k rate in between 79%-94%. Moreover, the effectiveness of the proposed system is also measured on the cross training datasets of same and different domain. The proposed system achieves a good high accuracy rate for same domain data sets and low accuracy rate for different domain datasets.

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