LAUSR

Understanding and predicting types of bugs are of practical importance for developers to improve the testing efficiency and take appropriate steps to address bugs in software releases. However, due to the complex conditions under which faults manifest and the complexity of the classification rules, the automatic classification of Mandelbugs is a difficult task. In this article, we present a deep semantic information-based Mandelbug classification method that combines a semantic model with a deep learning classifier and makes use of both labeled and unlabeled bug reports. By training the bug report semantic model on millions of bug reports, each word in the text of a bug report is represented as a word embedding that preserves the semantic relationship among the words. Then, a convolutional neural network model is designed to capture the high-level features of bug reports to obtain a more accurate classification. Moreover, the effects of the semantic model size and domain on the classification results are investigated, and the quality of word embeddings is evaluated by analyzing several important parameters.