<p>Assigning the most suitable developer to newly reported bugs present a significant challenge for triagers in the software bug life cycle. The diversity in bug components, severity, and priority, coupled with limited background knowledge on new bugs, often hinders efficient resolution. Traditional recommender systems and conventional machine-learning models struggle due to insufficient data on bugs and developers, making them inadequate for this task. While multi-armed bandit (MAB) and contextual multi-armed bandit (CMAB) approaches offer potential solutions, their reliance on limited historical data, slow learning, and rich contextual features restricts their effectiveness. This paper explores the impact of optimization methods such as Direct Method (DM), Inverse Propensity Scoring (IPS), Doubly Robust (DR), and Multi-Task Regression (MTR) on enhancing CMAB algorithm performance. Results demonstrate that these optimizers significantly improve average rewards across various CMAB algorithms across different α values. The proposed CMAB Triage model incorporates these optimizers, effectively addressing challenges such as reward bias, diverse contextual information to improve developer assignment, the cold-start problem, and the balance between exploration and exploitation. Evaluation results show that optimized CMAB models significantly enhance both efficiency and accuracy in developer recommendations for bug repositories. By integrating DM and IPS, our model reduces variance, adjusts for off-policy data, and provides robust, reliable recommendations, illustrating that optimization can realize the full potential of CMAB algorithms in bug triage. By demonstrating the robust performance of optimization-based CMAB models, this work highlights the critical role of optimization in realizing the full potential of CMAB algorithms in bug triage applications.</p>