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Reinforcement Learning in Bug Triaging: Addressing the Cold Start Problem and Beyond
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Author(s): Neetu Singh (Jaypee Institute of Information Technology, India)and Sandeep Kumar Singh (Jaypee Institute of Information Technology, India)
Copyright: 2024
Pages: 21
Source title:
Advancing Software Engineering Through AI, Federated Learning, and Large Language Models
Source Author(s)/Editor(s): Avinash Kumar Sharma (Sharda University, India), Nitin Chanderwal (University of Cincinnati, USA), Amarjeet Prajapati (Jaypee Institute of Information Technology, India), Pancham Singh (Ajay Kumar Garg Engineering College, Ghaziabad, India)and Mrignainy Kansal (Netaji Subhas University of Technology (NSUT), Delhi, India)
DOI: 10.4018/979-8-3693-3502-4.ch011
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Abstract
The bug cold start problem in software engineering arises when managing new bugs without historical data, challenging bug triaging systems. Reinforcement learning (RL) aids bug triaging, but conventional RL struggles with limited data. Advanced RL methods like bandits and DQN adapt to sparse data, enhancing decision-making. ML-based and RL-based approaches are explored to overcome this issue. Ethical concerns, interpretability, and exploration-exploitation trade-offs in RL are considered. Future research in RL shows promise in addressing the cold start problem across domains like bug triaging and e-commerce, with strategies such as improved exploration, transfer learning, hybrid approaches, and AutoML gaining traction.
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