BetterBench Alignment Roadmap

Spring AI Bench is working toward alignment with Stanford’s BetterBench 46-criteria framework for benchmark quality assessment. This is a self-assessment roadmap, not a validated certification.

1. Overview

BetterBench defines best practices across four benchmark lifecycle stages:

  1. Design (11 criteria) - Purpose, scope, metrics, interpretability

  2. Implementation (9 criteria) - Code availability, reproducibility, contamination resistance

  3. Documentation (14 criteria) - Process documentation, limitations, transparency

  4. Maintenance (3 criteria) - Code usability, feedback channels, support

This document tracks Spring AI Bench’s compliance with each criterion.

2. Design Stage (11 Criteria)

# Criterion Status Evidence/Notes

D1

User personas and use cases defined

✅ Met

Enterprise Java developers, DevOps teams. See Vision section

D2

Domain experts involved

✅ Met

Spring AI Community contributors with enterprise Java expertise, partnerships with prominent companies in development

D3

Integration of domain literature

✅ Met

BetterBench, SWE-bench critiques, industry best practices. See References

D4

Explanation of differences to related benchmarks

✅ Met

Comprehensive comparison in Comparison Table

D5

Informed choice of performance metric(s)

✅ Met

Multi-dimensional: success rate, cost, speed, reliability, quality. See Metrics

D6

Description of how benchmark score should/shouldn’t be interpreted

✅ Met

Context-dependent optimization explained (fastest/cheapest vs highest quality)

D7

Floors and ceilings for metric(s) included

⚠️ Partial

Planned: Baseline deterministic agent (floor), human performance level (ceiling)

D8

Human performance level included

📋 Planned

Q1 2025: Establish human baseline on representative tasks

D9

Random performance level included

⚠️ Partial

Hello-world deterministic agent serves as baseline (115ms, 100% success)

D10

Address input sensitivity

📋 Planned

Q1 2025: Document prompt variance testing across agents

D11

Validated automatic evaluation available

✅ Met

Judge API (deterministic + AI-powered). Judge framework in development

Design Score: 8/11 met, 2 partial, 1 planned (10.5/11 effective)

3. Implementation Stage (9 Criteria)

# Criterion Status Evidence/Notes

I1

Evaluation code available

✅ Met

GitHub: github.com/spring-ai-community/spring-ai-bench

I2

Evaluation data, prompts, or dynamic test environment accessible

✅ Met

Sample tasks published, extensible framework for custom tasks

I3

Script to replicate initial published results

✅ Met

One-click reproducibility: Docker containers with pre-configured environments + Maven test harness: ./mvnw test -Dtest=BenchHarnessE2ETest -pl bench-core

I4

Supports evaluation via API calls

✅ Met

Claude, Gemini, Amp via CLI integration (API-based models)

I5

Supports evaluation of local models

✅ Met

Via Spring AI Agents abstraction layer

I6

Globally unique identifier or encryption of evaluation instances

📋 Planned

Q2 2025: Implement task instance identifiers

I7

Inclusion of training_on_test_set task

📋 Planned

Q2 2025: Contamination detection task

I8

Release requirements specified

✅ Met

Versioning strategy, semantic versioning for benchmark tasks

I9

Build status indicator (e.g., GitHub Actions)

✅ Met

CI/CD pipeline validates builds on each commit

Implementation Score: 6/9 met, 0 partial, 3 planned (6/9 effective)

4. Documentation Stage (14 Criteria)

# Criterion Status Evidence/Notes

D1

Documentation of benchmark design process

✅ Met

Architecture docs explain design rationale. See Architecture

D2

Documentation of data collection, prompt design, or environment design

✅ Met

Sandbox architecture documented in codebase

D3

Documentation of test task categories and rationale

✅ Met

Enterprise workflow alignment explained. See What Makes Us Different

D4

Documentation of evaluation metric(s)

✅ Met

Judge framework, multi-dimensional scoring documented in index

D5

Report statistical significance of results

⚠️ In Progress

Q1 2025: Add variance analysis, confidence intervals for multi-run protocols

D6

Documentation of normative assumptions

✅ Met

BetterBench alignment statement. See this document and Standards

D7

Documentation of limitations

⚠️ Partial

Current scope stated, expanding limitations section in Q1 2025

D8

Requirements file

✅ Met

Maven pom.xml with all dependencies

D9

Quick-start guide or demo code

✅ Met

See Getting Started Guide

D10

Code structure description

✅ Met

See Architecture Overview

D11

Inline comments in relevant files

✅ Met

Codebase follows Java documentation standards

D12

Paper accepted at peer-reviewed venue

📋 Planned

Target: NeurIPS Datasets & Benchmarks 2025 submission

D13

Accompanying paper publicly available

📋 Planned

Will be published upon NeurIPS acceptance

D14

License specified

✅ Met

Apache License 2.0 (GitHub repository)

Documentation Score: 10/14 met, 2 partial, 2 planned (11/14 effective)

5. Maintenance Stage (3 Criteria)

# Criterion Status Evidence/Notes

M1

Code usability checked within last year

✅ Met

Active development, regular CI builds

M2

Maintained feedback channel for users

✅ Met

GitHub Issues, Discussions. See Issues

M3

Contact person identified

✅ Met

Maintainer team listed in README, CONTRIBUTORS.md

Maintenance Score: 3/3 met (100%)

6. Summary Score

Lifecycle Stage Met Partial Planned Score (Effective)

Design (11 criteria)

8

2

1

10.5/11 (95%)

Implementation (9 criteria)

6

0

3

6/9 (67%)

Documentation (14 criteria)

10

2

2

11/14 (79%)

Maintenance (3 criteria)

3

0

0

3/3 (100%)

TOTAL (37 assessed)

27

4

6

30.5/37 (82%)
These scores represent our self-assessment against BetterBench criteria. This is not a validated BetterBench certification, but a roadmap for continuous improvement.

7. Roadmap to Full Alignment

7.1. Q1 2025 (Priority: High)

  • Add statistical significance reporting

    • Document variance analysis methodology

    • Report confidence intervals for multi-run protocols

    • Establish statistical testing procedures

  • Document input sensitivity testing

    • Prompt variance testing across agents

    • Consistency checks for equivalent formulations

  • Establish human baseline

    • Select representative tasks

    • Measure human developer performance

    • Document methodology

  • Expand limitations documentation

    • Current scope boundaries

    • Known constraints

    • Future roadmap

7.2. Q2 2025 (Priority: Medium)

  • Implement unique task identifiers

    • UUID-based task identification

    • Enable contamination tracking

  • Add training-on-test-set detection

    • Canary tasks to detect memorization

    • Contamination analysis framework

  • Submit to NeurIPS Datasets & Benchmarks

    • Prepare manuscript

    • Submit by deadline

    • Address reviewer feedback

7.3. Q3-Q4 2025 (Priority: Lower)

  • Publish peer-reviewed paper

    • Complete NeurIPS review process

    • Make paper publicly available

    • Cite in documentation

  • Establish floor/ceiling baselines

    • Naive baseline (random, deterministic)

    • Expert human performance ceiling

    • Continuous monitoring

8. Self-Assessment Updates

This alignment roadmap is maintained as a living document and updated quarterly:

  • Last Updated: 2025-10-06

  • Next Review: 2026-01-06

  • Maintainer: Spring AI Bench Team

  • Contact: GitHub Issues

  • Important: This is self-assessment, not official BetterBench validation

9. BetterBench Resources

10. Commitment Statement

Spring AI Bench commits to:

  1. Working toward BetterBench alignment as a quality improvement goal

  2. Transparency in methodology, scoring, and limitations

  3. Continuous improvement using BetterBench principles as guidance

  4. Community engagement to refine criteria and implementation

  5. Quarterly reviews of this self-assessment roadmap

  6. Honest representation: This is aspiration and self-assessment, not official validation

We welcome community feedback on our alignment efforts. Please use GitHub Issues to suggest improvements or identify gaps.