The field of software engineering is moving towards a deeper understanding of the interplay between technical debt, community smells, and microservices. Recent studies have highlighted the importance of early detection and mitigation of socio-technical issues to maintain the long-term quality and sustainability of machine learning-based systems and microservice architectures. The use of language models and distribution testing approaches has shown promise in attributed code generation and authorship attribution. Noteworthy papers include:

• The Technical Debt Gamble, which explores technical debt in large-scale microservice architectures and identifies strategies for its management.
• Reassessing Code Authorship Attribution in the Era of Language Models, which conducts an extensive empirical study on the effectiveness of transformer-based language models in code authorship attribution.