AI-Powered Software Bug Tracking Tool
| dc.contributor.author | Ahmad Haizar bin Sahmat | |
| dc.date.accessioned | 2026-04-24T08:48:35Z | |
| dc.date.issued | 2025 | |
| dc.description | Most IT companies still rely on manual approaches to manage and track software bugs during testing. This traditional method is time-consuming, error-prone, and lacks efficiency particularly in large or complex software projects. To address these challenges, this project proposes the development of an AI-powered Software Bug Tracking Tool designed to streamline the process of reporting, tracking, categorizing, and prioritizing bugs. By leveraging Artificial Intelligence (AI) technologies, including machine learning, the system can automatically classify bugs based on their severity, providing valuable insights for better decision-making. Additionally, the system employs unsupervised learning techniques to detect patterns and relationships among bugs. An OpenAI GPT-4o model, accessed via the Azure OpenAI Service, is integrated into the system to automatically generate summaries of bug reports, helping teams quickly understand the key details. A user-friendly design is also provided to support collaboration between developers and testers, reducing the learning curve and improving overall software quality. The implementation of this system aims to enhance team productivity, ensure comprehensive bug tracking, and align with modern software development practices. Based on the usability testing with 10 participants, including software expert from various background such as software testers and developers, both participants and software expert found the duplicate detection and bug summarization features particularly useful, as they helped streamline the bug tracking process and improve understanding of reported issues. However, the bug severity prediction feature produced unexpected classifications in certain cases, indicating the need for additional training data and the potential integration of reinforcement learning to enable the model to continuously improve over time. | |
| dc.identifier.uri | https://scholarhub.unimas.my/handle/123456789/481 | |
| dc.language.iso | English | |
| dc.publisher | Universiti Malaysia Sarawak | |
| dc.relation.ispartofseries | Faculty of Computer Science and Information Technology | |
| dc.title | AI-Powered Software Bug Tracking Tool | |
| dc.type | Final Year Project |