Benchmark Overview

Spring AI Bench benchmarks are designed to evaluate AI agents on real-world Java software engineering tasks.

1. What Makes a Good Benchmark?

Effective benchmarks for AI developer agents should:

  • Reflect Real Workflows - Mirror actual developer tasks and processes

  • Use Representative Codebases - Work with typical enterprise Java projects

  • Measure Practical Skills - Test abilities that matter in production

  • Avoid Overfitting - Resist gaming through memorization

  • Enable Custom Evaluation - Work on your own codebases

2. Current Implementation Status

2.1. âś… Available Now

hello-world Track: Infrastructure validation and basic agent testing * File creation and basic I/O operations * Multi-agent comparison (deterministic vs AI-powered) * Foundation for future track development

2.2. 🚧 Planned Benchmark Categories

These represent the enterprise Java workflows that will differentiate Spring AI Bench from Python-focused benchmarks:

2.2.1. Coding Tasks

Software development activities that agents should be able to perform:

  • Bug Fixing - Diagnose and fix failing tests or runtime issues

  • Feature Implementation - Add new functionality based on specifications

  • Refactoring - Improve code structure while preserving behavior

  • API Migration - Update code to use newer APIs or frameworks

2.2.2. Project Management

Higher-level tasks that demonstrate planning and coordination:

  • Issue Triage - Analyze bug reports and feature requests

  • PR Review - Evaluate code changes for quality and correctness

  • Test Coverage - Identify and write tests for uncovered code

  • Documentation - Generate or update technical documentation

2.2.3. Version Upgrade

Complex tasks involving dependency and framework management:

  • Dependency Upgrades - Update libraries while fixing breaking changes

  • Framework Migration - Move between framework versions (e.g., Spring Boot 2→3)

  • Java Version Upgrade - Update codebases to newer Java versions

  • Build System Migration - Convert between build tools (e.g., Maven→Gradle)

3. Benchmark Structure

3.1. YAML Specification

Each benchmark is defined by a YAML file:

# hello-world.yaml - Current working example
id: hello-world
category: coding
version: 0.5

repo:
  owner: example-org
  name: example-repo
  ref: main

agent:
  kind: hello-world-ai
  provider: claude
  autoApprove: true
  prompt: |
    Create a hello world file with the specified content.

success:
  cmd: ls hello.txt

timeoutSec: 600

3.2. Component Breakdown

3.2.1. Repository Specification

  • Owner/Name - GitHub repository coordinates

  • Ref - Specific commit, branch, or tag to checkout

  • Subpath - Optional subdirectory focus (for monorepos)

3.2.2. Agent Configuration

  • Kind - Agent type (currently: hello-world, hello-world-ai)

  • hello-world - Deterministic mock agent for testing

  • hello-world-ai - AI-powered agent via Spring AI Agents integration

  • Provider - AI provider for hello-world-ai (claude, gemini)

  • Prompt - Natural language task description

  • Auto-approve - Whether to bypass human confirmation prompts

3.2.3. Success Criteria

  • Command - Shell command to verify success

  • Exit Code - Expected exit code (usually 0)

  • Timeout - Maximum time for verification

  • File Checks - Optional file existence/content verification

4. Evaluation Metrics

4.1. Primary Metrics

  • Success Rate - Percentage of benchmarks completed successfully

  • Time to Completion - Duration from start to successful completion

  • Attempt Efficiency - Ratio of successful attempts to total attempts

4.2. Secondary Metrics

  • Code Quality - Style, maintainability, and best practices

  • Test Coverage - Percentage of code covered by tests

  • Security Compliance - Absence of security vulnerabilities

  • Resource Usage - CPU, memory, and network consumption

5. Benchmark Design Principles

5.1. Realistic Scenarios

Benchmarks should represent tasks that developers actually perform:

  • Use real codebases with typical complexity

  • Include context and constraints found in production

  • Require multi-step reasoning and planning

  • Test tool usage and command-line interaction

5.2. Evaluation Robustness

Prevent gaming and ensure fair evaluation:

  • Use multiple repositories for each task type

  • Include negative cases that should fail

  • Test edge cases and error conditions

  • Rotate repository versions to prevent memorization

5.3. Scalability

Design for efficient execution and maintenance:

  • Support parallel execution of multiple benchmarks

  • Enable batch processing for large benchmark suites

  • Provide filtering and tagging for selective execution

  • Include cleanup and isolation to prevent interference

6. Multi-Agent Benchmarking

Spring AI Bench supports comparative testing between different agent types, enabling performance analysis across deterministic and AI-powered agents.

6.1. Comparative Testing

Multi-agent benchmarks run the same task across different implementations:

  • Deterministic Implementation - hello-world agent provides fast, predictable baseline

  • AI-Powered Implementation - hello-world-ai agent uses spring-ai-agents framework

    • Claude provider for complex reasoning tasks

    • Gemini provider for balanced speed/capability

  • Direct CLI Agents - claude-code and gemini for direct integration

6.2. Performance Metrics

Key metrics for multi-agent comparison:

  • Execution Time - Duration from start to completion

  • Success Rate - Percentage of successful task completions

  • Performance Ratio - Relative speed compared to baseline

  • Accuracy - Quality and correctness of output

6.3. Integration with Spring AI Agents

The hello-world-ai agent type provides end-to-end integration with the Spring AI Agents framework:

  • JBang Launcher - Seamless execution via JBang command-line tool

  • Provider Selection - Choose between Claude, Gemini, or other AI providers

  • Local Testing - Full integration testing without external dependencies

  • Report Generation - Comprehensive HTML reports with agent metadata

7. Next Steps