Added LLAMOSC Framework

[sarrah-basta]

LLAMOSC stands for LLM Agent-Based Modeling for Open Source Communities. The name encapsulates the core components and objectives of the framework:

• LLM: Large Language Models, the backbone of our intelligent agents, providing them with advanced perception, reasoning, and decision-making capabilities.
• ABM: Agent-Based Modeling, the methodological approach we use to simulate the interactions and dynamics within open-source communities.
• OSC: Open Source Communities, the focus of our simulations, aiming to enhance sustainability, collaboration, and engagement within these communities.

Key Components and Features implemented so far

1. Environment:

   • GitHub (CodeSpace Environment): A simulated code space (powered by AutoCodeRover) where agents contribute to open-source projects, mirroring real-world GitHub repositories.

2. Environment Variables:

   • Issues: Issues are categorized with varying difficulty from 1 to 5, resembling real-life variation from "Good First Issues" to "Expert Required Issues", assigned based on the contributor agent's experience level.

3. Agents:

   • Coding Ability: Skill level in writing and reviewing code, (powered by AutoCodeRover).

4. Agent Variables (Internal States):

   • Experience Level: Categorized from 1 to 5 resembling real-life contributors ranging from Novice to Experienced.

Framework Capabilities

LLAMOSC integrates multiple capabilities to automate and optimize open-source community activities:

1. AutoCodeRover Integration:

   • Combines LLMs with advanced code navigation and debugging capabilities.
   • Operates in two stages: Context retrieval and Patch generation.
   • Features Program Structure Aware code search APIs to navigate codebases and generate patches.

2. Automated Pull Request Lifecycle:

   • ContributorAgent: Automates issue identification, solution proposal, and pull request creation.
   • MaintainerAgent: Automates pull request review and merging based on predefined criteria.
   • Utilizes Docker for environment consistency and isolation.
   • Implements diff extraction and automated pull request submission (powered by AutoCodeRover).

3. Multi-Agent Decision Making Algorithms:

   • Authoritarian Algorithm (Benevolent Dictator Model):
     • Central maintainer manages and allocates tasks.
     • Contributors are rated and assigned tasks based on maintainer’s discretion.
   • Decentralized Algorithm (Meritocratic Model):
     • Distributed decision-making among contributors.
     • Contributors bid on tasks based on their suitability and experience.

Accomplishments of LLAMOSC so far

• Preliminary Design and Implementation:

  • Created a robust framework for simulating open-source environments.
  • Developed agents with varying skill levels and experience to reflect real-world dynamics.

• Automated Pull Request Lifecycle:

  • Streamlined the process of identifying, solving issues, creating, submitting, reviewing, and merging pull requests.
  • Implemented reliable and reproducible workflows for ContributorAgent and MaintainerAgent.

• Multi-Agent Decision Making:

  • Simulated governance models used in open-source projects.
  • Developed algorithms for both centralized (authoritarian) and decentralized (meritocratic) task allocation.

[Orthogonal-Research-Lab]

Thanks for your contribution!