The severity of software bug reports plays an important role in maintaining software quality. Many approaches have been proposed to predict the severity of bug reports using textual information. In this research, we propose a deep learning framework called MASP that uses convolutional neural networks (CNN) and the content-aspect, sentiment-aspect, quality-aspect, and reporter-aspect features of bug reports to improve prediction performance. We have performed experiments on datasets collected from Eclipse and Mozilla. The results show that the MASP model outperforms the state-of-the-art CNN model in terms of average Accuracy, Precision, Recall, F1-measure, and the Matthews Correlation Coefficient (MCC) by 1.83%, 0.46%, 3.23%, 1.72%, and 6.61%, respectively.
In this paper, we propose a method to automatically generate source code files from a use case model and a domain class diagram named USLSCG (Use case Specification Language (USL) based Code Generation). In our method, a use case scenario is precisely specified by a USL model. The USL model and the domain class diagram then are used as inputs to generate source code files automatically. These source code files include classes following three-layer applications and a SQL script file to create a database and store procedures.
The processing priorities for software bug reports are important for software maintenance. Predicting the priorities for bug reports is the subject of many software engineering studies. This study proposes a priority prediction method that uses comment intensiveness features and a Synthetic Minority Over-sampling Technique (SMOTE)-based data balancing scheme. Experiments use datasets for three open-source projects: Eclipse, Mozilla and OpenOffice. The effectiveness of the proposed approach is determined using five classification models: Multinomial Naïve Bayes, Support Vector Machines, Random Forest, Extra Trees and eXtreme Gradient Boosting. The results show that the CIS-SMOTE-based models achieve 0.6078 Precision, 0.4927 Recall, 0.4465 F1-score and 0.7836 Accuracy in priority perdition. The results also show that CIS-SMOTE-RF, CIS-SMOTE-ET and CIS-SMOTE-XGB outperform two advanced priority prediction approaches, eApp and cPur, in terms of all performance measures.
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