CodePori uses a multi-agent system to automate code generation and surveys practitioners on its usability in real-world software development
The study proceeded in two phases: it built a multi-agent system named CodePori to automate code generation, then used a survey in which participants evaluated the agents' performance on autonomous software development tasks; the results indicate that LLM-based multi-agent systems show potential for autonomous code generation, but successful integration requires addressing specific challenges such as short-term memory limitations, hallucinations, and code smells, and incorporating a practitioner-centric perspective.