Research Article AnAutomatedApproachforthePredictionoftheSeverityLevelof Bug Reports Using GPT-2 Mohsin kamal , 1 Sikandar Ali , 2 Anam Nasir , 1 Ali Samad , 3 Samad Basser , 4 and Azeem Irshad 5 1 Department of Computer Science, COMSATS University, Islamabad, Pakistan 2 Department of Information Technology, e University of Haripur, Haripur 22620, Khyber Pakhtunkhwa, Pakistan 3 Faculty of Computing, e Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan 4 Department of Computer System Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan 5 Department of Computer Science and Software Engineering, International Islamic University, Islamabad, Pakistan Correspondence should be addressed to Sikandar Ali; sikandar@uswat.edu.pk and Azeem Irshad; irshadazeem2@gmail.com Received 8 January 2022; Revised 21 April 2022; Accepted 4 May 2022; Published 29 May 2022 Academic Editor: Muhammad Arif Copyright © 2022 Mohsin kamal et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Manual investigation is warranted in traditional approaches for estimating the bug severity level, which adds to the effort and time required. For bug severity report prediction, numerous automated strategies have been proposed in addition to manual ones. However, the current bug report predictors by facing several issues, such as overfitting and weight computation, and therefore, their efficiency for specific levels of data noise needs to improve. As a result, a bug report predictor is required to solve these concerns (e.g., overfitting and avoiding weight calculation, which increases computing complexity) and perform better in the situation of data noise. We use GPT-2’s features (limiting overfitting and supplying sequential predictors rather than weight computation) to develop a new approach for predicting the severity level of bug reports in this study. e proposed approach is divided into four stages. First, the bug reports are subjected to text preprocessing. Second, we assess each bug report’s emotional score. ird, each report is presented in vector format. Finally, an emotion score is assigned to each bug report, and a vector of each bug report is produced and sent to GPT-2. We employ statistical indicators like recall, precision, and F1-score to evaluate the suggested method’s effectiveness and efficacy. A comparison was also made using state-of-the-art bug report predictors such as Random Forest (RF), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) Network, Support Vector Machine (SVM), XGBoost, and Naive Bayes Multinomial (NBM). e proposed method’s promising result indicates its efficacy in bug information retrieval. 1. Introduction As indicated by the examination local area, the quality and strength of the product advancement are related to the data extricated from the bug reports [1]. Regularly, bug reports are chosen in light of the seriousness level of bugs. us, the seriousness level characteristic of a bug report is the most essential element for getting the progression and upkeep sorted out, particularly in Free Open-Source Programming [2]. e seriousness level gauges the impact that a bug has on a useful execution of the product framework and how quickly a bug ought to be coordinated to the advancement group. Both scholar and business networks have made an expansive request to automate the bug seriousness expectation. e information extracted from the bug reports plays a vital role in the development of the software, software evolution, and maintenance tasks. Bug reports are registered by the users in the response of their usage. Meanwhile, developers took the responsibility of extracting knowledge from these reports in order to evolve their systems effec- tively. ere are several bug tracking systems (BTS) such as JIRA and Bugzilla, and they are considered as a bug report repository [3]. Generally, bug reports are often submitted by Hindawi Security and Communication Networks Volume 2022, Article ID 2892401, 11 pages https://doi.org/10.1155/2022/2892401