The Impact of GloVe and Word2Vec Word-Embedding Technologies on Bug Localization with Convolutional Neural Network

Abstract

In the field of software engineering, software quality assurance faces many challenges, including overcoming the problem of identifying errors in the source code. Finding the location of the error in the source code is a very important process, as is taking advantage of the semantic information available in the bug reports and the source code to find the similarities between them, using modern techniques called word embedding. This study aims to demonstrate how GloVe and Doc2Vec word-embedding technologies affect bug localization accuracy and performance. Therefore, this study proposes to adapt DeepLoc by using GloVe embedding techniques to process the source code instead of Word2vec and using Word2vec embedding techniques to process the bug report instead of Sent2Vec. AspectJ represents the large dataset, which contains many bug reports, while SWT's small dataset contains fewer bug reports. Experimental results show that the improved DeepLoc on SWT achieves 0.60 and 0.72 MAP and MRR, respectively. While the improved DeepLoc on AspectJ achieves 0.17 and 0.27 MAP and MRR, respectively. The results of the improved DeepLoc should be compared using two advanced models from previous studies: DeepLoc, DeepLocator.