From iron to diamond: Collaborative behavior development across competitive tiers in League of Legends

Mass collaborative product development (MCPD) is an emerging product development mode which makes full use of open group intelligence with the advantage of low cost and high innovation level. In this paper, behaviors of individuals in MCPD, especially collaborative behaviors, are modeled and simulated, and the effects of collaborative behaviors on the whole MCPD process are identified based on the simulation study. Firstly, a multi-agent process model of MCPD is built, which includes product model, community model, and three-level agent model. Secondly, collaboration behaviors including collaboration request, collaborator choosing, collaboration judgment, and collaboration response are depicted. Finally, based on the process model and the individual behaviors of MCPD, a simulation system for MPCD is designed and developed, and two sets of experiments are carried out. The results of experiments show that different weights of collaborator choosing indices significantly influence OSC evolution, and individuals' collaboration intensions significantly influence product evolution. The findings of this paper are expected to help the community managers develop more reasonable management and service mechanisms for participants of the MCPD.

The advent of Large Language Models (LLMs), including tools like GitHub Copilot and OpenAI Codex, has brought substantial changes to the field of software engineering. These technologies support developers through features such as automated code generation, smart code suggestions, and productivity enhancements. Despite these advancements, the development workflow is still scattered across multiple standalone tools used for coding, testing, documentation, and team communication. This lack of integration disrupts the development flow and negatively impacts overall team efficiency. To address these challenges, this paper proposes the AI-Driven Developer Ecosystem (AIDE)—a comprehensive development framework that harnesses the capabilities of LLMs while addressing gaps in tool interoperability and contextual awareness. AIDE functions as a unified, intelligent development environment that offers AI-assisted coding, predictive insights for continuous integration and deployment (CI/CD), automated issue classification, adaptive system architecture analysis, and harmonized documentation tools. AIDE sets itself apart from conventional development environments by providing continuous, context-aware support. It does this by analyzing real-time code changes, historical data, and team collaboration behavior. The platform also integrates collaborative tools such as Excalidraw for visual planning and embedded communication features for real-time coordination, promoting a deeply collaborative development experience that extends beyond code writing. By drawing from current academic research and industry practices, this paper illustrates how AIDE effectively addresses critical issues in intelligent software development, resulting in better code quality, minimized downtime, and increased developer satisfaction.