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OctoCodingBench - MiniMax's Open-Source Coding Agent Evaluation Suite

Author: AI小集 · Original source: AI工具集

What is OctoCodingBench

OctoCodingBench is MiniMax's open-source evaluation suite for the instruction-following capability of Coding Agents. By simulating real-world software development scenarios, OctoCodingBench evaluates from multiple dimensions—including system constraints, user instructions, project specifications, skill invocation, and historical memory—whether an Agent can strictly follow rules to complete tasks. Unlike traditional evaluations that focus only on results, OctoCodingBench places greater emphasis on process compliance, using two metrics—Check-level accuracy (CSR) and Instance-level success rate (ISR)—to meticulously measure an Agent's performance under complex constraints. The evaluation suite contains 72 instances covering a variety of development scenarios, driving the evolution of Coding Agents from "being able to write code" to "being able to collaborate in a standardized way."

OctoCodingBench

Main Features of OctoCodingBench

  • Multi-dimensional instruction-following evaluation: Comprehensively evaluates whether an Agent can strictly follow rules to complete tasks across multiple dimensions, including system constraints (such as language style and tool usage), user instructions (multi-turn interaction), project specifications (such as code style and testing process), skill invocation, and historical memory.
  • Separating task completion from rule following: Uses two metrics—Check-level accuracy (CSR) and Instance-level success rate (ISR)—to measure an Agent's performance under individual constraints and all constraints respectively, revealing the Agent's process compliance.
  • Real-world development scenario simulation: Provides 72 carefully designed instances, each containing a natural language user query, system prompt, project documentation, skill documentation, etc., simulating a real software development task environment.
  • Conflict detection and resolution capability testing: Designs scenarios containing conflicting instructions to evaluate an Agent's priority judgment and conflict resolution capability when facing contradictory instructions.
  • Support for multiple development frameworks: Provides configurations and Docker environments for multiple development frameworks (such as Claude Code, Kilo, and Droid), ensuring that evaluation is conducted in a real production environment.
  • Binary checklist scoring: Each evaluation item is objectively determinable (pass/fail), ensuring the transparency and reproducibility of evaluation results.

Technical Principles of OctoCodingBench

  • Multi-source instruction system: OctoCodingBench divides instruction sources into 7 categories, including system prompts, system reminders, user queries, project documentation (such as CLAUDE.md and AGENTS.md), skill documentation, historical memory, and tool invocation specifications. Each category has a different authority level and constraint content.
  • Structured evaluation checklist: Each evaluation instance comes with a detailed structured evaluation checklist (Checklist), containing multiple binary determinable evaluation items (Check-item). The evaluation items cover all aspects from language style to code implementation.
  • Docker environment simulation: To ensure environment consistency for evaluation, OctoCodingBench provides 34 different Docker images, each simulating a real development environment. The development environment includes project code, dependency libraries, testing tools, etc., ensuring that the Agent runs under conditions consistent with an actual development environment.
  • LLM-as-Judge scoring mechanism: Uses a large language model (LLM) as the scoring tool to evaluate the Agent's behavior trajectory item by item. Based on a predefined evaluation checklist, the LLM determines whether the Agent satisfies each constraint condition, giving a pass or fail judgment.
  • Data collection and trajectory analysis: During the evaluation process, the system collects the complete interaction trajectory of the Agent, including system prompts, user queries, the Agent's responses, tool calls, etc. The trajectory data is used for subsequent scoring analysis.
  • Statistics and Analysis: By calculating the two metrics CSR and ISR, a quantitative analysis of the Agent's overall performance is conducted. CSR measures the Agent's accuracy on individual constraints, and ISR measures the success rate when all constraints are satisfied simultaneously.

Project address of OctoCodingBench

  • HuggingFace Model Library：https://huggingface.co/datasets/MiniMaxAI/OctoCodingBench

Application scenarios of OctoCodingBench

  • Agent Development and Training: Through standardized evaluation, optimize the training process of Coding Agents to ensure excellent performance in instruction following and process compliance, not just generating correct code.
  • Software Engineering and Development: Evaluate whether Coding Agents follow project specifications (such as code style and testing processes), improve code quality, and ensure team collaboration efficiency.
  • Academic Research and Evaluation: Provide a standardized benchmark to compare the performance of different Coding Agent models in instruction following and process compliance, providing data support for research directions.
  • Education and Training: Help developers and students understand the behavior patterns of Coding Agents, learn how to design effective instructions, and improve their application capabilities in actual development.