2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 18 , Issue 1 , PP: 260-273, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Khasimbee Shaik 1 * , K .V. Satyanarayana 2 , Tirimula Rao Benala 3
Doi: https://doi.org/10.54216/JISIoT.180120
Software quality assurance teams can increase productivity and efficiency by expediting the issue-fixing process through automatic localization of bug files. Although source code and bug reports provide valuable semantic information, current bug localization techniques typically underuse it. Numerous deep learning and word embedding models have been developed over time. The word-embedding model used to represent bug reports and the deep learning model used for categorization determine how effective those methods are. Aim of this research is to construct word-embedding method, which has been automated for bug detection using deep learning techniques. Here the input data has been collected as software design based monitored data and processed. Then this data has been analyzed using Bi-LSTM voting vector word embedding model and the feature classification is carried out using convolutional naïve bays attention perceptron neural network in bug detection model. The experimental analysis is carried out in terms of training accuracy, precision, Mean square error, F-1 score, and recall. Furthermore, cross-training datasets from the same and distinct domains are used to gauge how effective the suggested approach is. For datasets in the same domain, suggested system obtains a good high accuracy rate; for datasets in separate domains, it achieves a poor accuracy rate.
Word embedding model , Bug detection , Deep learning techniques , Bi-LSTM voting vector , Convolutional naï , ve bays
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