The field of automated bug management and Open-Source Software (OSS) ecosystems is witnessing significant advancements, driven by innovative applications of machine learning and natural language processing techniques. Researchers are developing novel approaches to automatically detect duplicate bug reports, prioritize bug reports, and predict bug-related outcomes, leveraging methods such as topic modeling, deep learning, and sentiment analysis. These advancements have the potential to improve the efficiency and effectiveness of bug management in large open-source projects. Furthermore, studies are exploring the interplay between developer sentiment and workforce dynamics in OSS ecosystems, highlighting the importance of sentiment-driven strategies for managing project sustainability and innovation. Noteworthy papers in this area include:

• Automated Duplicate Bug Report Detection in Large Open Bug Repositories, which proposes a novel threshold-based approach for duplicate identification.
• Bug Destiny Prediction in Large Open-Source Software Repositories through Sentiment Analysis and BERT Topic Modeling, which demonstrates the value of sentiment analysis in predicting bug outcomes.
• Exploring turnover, retention and growth in an OSS Ecosystem, which investigates the impact of developer sentiment on workforce dynamics in the Gentoo ecosystem.