Software maintenance is an essential phase of software development. Developers employ issue tracking systems to collect bugs for software improvement. Users submit bugs through such issue tracking systems and decide the severity of reported bugs. The severity is an important attribute of a bug that decides how quickly it should be solved. It helps developers to solve important bugs on time. However, manual severity assessment is a tedious job and could be incorrect. To this end, in this paper, we propose a deep neural network-based automatic approach for the severity prediction of bug reports. First, we apply natural language processing techniques for text preprocessing of bug reports. Second, we compute and assign an emotion score for each bug report. Third, we create a vector for each preprocessed bug report. Forth, we pass the constructed vector and the emotion score of each bug report to a deep neural network based classifier for severity prediction. We also evaluate the proposed approach on the history-data of bug reports. The results of cross-product suggest that the proposed approach outperforms the state-of-the-art approaches. On average, it improves the f-measure by 7.90%.

An app store (i.e., Google Play) is a platform for mobile apps for almost every software and service. App stores allow users to browse and download apps and facilitate developers to keep an eye on their apps by providing ratings and reviews of the apps. App reviews may include the user's experience, information about bugs, request for new features, or rating of the app in word. The manual categorization of app reviews is critical and time-consuming for developers. Automatic classification of app reviews may help developers especially for fixing bugs on time. In this perspective, several approaches have been proposed for the automatic classification of reviews. However, none of them exploits the non-textual information of app reviews. In this paper, we propose a deep learning based approach for the classification of app reviews. It does not only leverage non-textual information of app reviews but also exploits a deep learning technique that has proved more accurate for the text classification in various domains. The approach first extracts textual and nontextual information of each app review, preprocesses the textual information, computes the sentiment of app reviews using Senti4SD, and determines the history of the reviewer includes the total number of reviews posted by the reviewer, and his submission rate (i.e., what percentages of his review have been submitted for the associated app). Second, we create a digital vector against each app review. Finally, we train a deep learning based multi-class classifier to classify app reviews. The proposed approach is evaluated on a public dataset, and the results suggest that it significantly improves the state of the art. It improves average precision from 75.72% to 95.49%, average recall from 69.40% to 93.94%, and f-measure from 72.41% to 94.71%, respectively.

Mechanical failures in rotating machinery (e.g. wind turbines, generators, motor-derives etc.) may result in catastrophic failures. Different mechanical faults induce characteristic vibrations in the equipment structure. Online vibration monitoring helps mitigate catastrophic failures through early detection and identification of underlying mechanical faults. However, extracting characteristic vibration features that improve fault classification performance and are robust to various noises in the vibration signals is a challenging task. Various statistical and signal processing-based vibrationfeatures have been proposed in the literature. These vibrationfeatures were devised on the basis of prior knowledge about characteristics of vibration signals from different fault types. Recently, automatic feature extraction through unsupervised learning in deep neural architectures has resulted in state of art performance on image and speech recognition tasks. So, Instead of feature-engineering, we, here, hypothesized that feature learning on raw vibration signal possibly will extract vibration-features that can improve fault identification performance of subsequent classifier. To the purpose, we explored Convolutional Neural Network for unsupervised feature learning on vibration signals and Denoising AutoEncoder for extracting vibration features that are robust and invariant to the noises in vibration signals. We proposed a Hybrid deep-model consisting of a Multi-channel Convolutional Neural Network followed by a stack of Denoising Auto-Encoders (MCNN-SDAE) with a single classification layer at the top. We compared the fault identification and classification performance of the proposed model with other models employing tradition statistical and signal processing based vibration-features. We validated the performance of all models on a benchmark vibration data collected from an experimental test-rig specifically designed to study vibration characteristics of bearing related faults.

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Summary Most of the data concerning business‐oriented systems are still based on either NoSQL or the relational data model. On the other hand, Semantic Web data model Resource Description Framework (RDF) has become the new standard for data modeling and analysis. Due to this situation integration of NoSQL, Relational Database (RDB) and RDF data models are becoming a required feature of the systems. Many solutions like tools and languages are provided in the shape of the transformation of data from RDB to RDF. This research is aimed to compare and map data models used for transformation between NoSQL, RDB, and Semantic Web. This study will help in achieving much better and enhanced technology‐based systems for retrieval and storage of data among Big‐data annotation using Semantic Web. It is aimed to reduce the response time of queries and offer compatibility with the web and semantically enriched data format. A drugs dataset is being used and transformed to have semantical meaning embedded and linked to support big data localization. At the end of this paper, RDF graph and bar chart are used to represent transformed data after passing through the proposed model. Big data localization helps in gaining fast and accurate results.

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