GSoC 2026 - Interest in "Behavioral Evaluation Test Framework" Project

[canemirbora4]

Hi @gundermanc ,

My name is Can Emir Bora, and I am a computer engineering undergraduate at Bogazici University in Istanbul, Turkey. I am preparing to apply for GSoC 2026, and I am particularly interested in contributing to the “Behavioral Evaluation Test Framework” project.

My background aligns strongly with this project, combining software engineering, infrastructure test automation, and AI/ML research. During my recent software engineering internship, I built a serverless test automation framework from scratch using Go and AWS Lambda. In addition, I worked as a research intern on data-driven and simulation-based systems, including dataset collection and analysis pipelines for computer vision and robotics research. My ongoing academic research focuses on neurosymbolic learning in robotics. These experiences motivated my interest in designing systematic evaluation and benchmarking tools for intelligent, non-deterministic systems.

I have started exploring the Gemini CLI repository and thinking about a possible architecture for this evaluation framework. Currently, I am considering an approach that involves:

• Defining a structured evaluation harness capable of simulating different CLI environments and states,
• Establishing baseline success metrics (for example, AST-based validation for code-generation tasks or execution exit codes for debugging scenarios),
• Integrating regression reporting into CI/CD pipelines to ensure agent behavior remains stable across updates.

Before drafting my proposal, I would like to align with the team’s expectations and preferred direction. Could you please advise:

• Whether there is an existing discussion, issue, or design direction I should review,
• If contributors are expected to prototype parts of the evaluation framework before proposal submission,
• Any recommended starting point for getting involved early?

I would be happy to begin contributing immediately and refine my proposal based on your guidance. Thank you for your time, and I look forward to your feedback.

Best regards,
Can Emir Bora
GitHub: https://github.com/canemirbora4
LinkedIn: https://linkedin.com/in/canemirbora/

[hscecoder]

[canemirbora4]

https://summerofcode.withgoogle.com/programs/2026/organizations
https://docs.google.com/document/d/1iaMZliqwUn-ACyZAbgzdXmDiQZ7l5gp8UQIIY2BnPO8/edit?tab=t.0#heading=h.4zvukoza0gln

[hscecoder]

Where you found this?

[canemirbora4]

The link is directly available on the GSOC homepage.

[shathwik30]

Hi @gundermanc ,

I'm Shathwik, a CS student currently in college at
BITS Pilani. I'm interested in the Behavioral Evaluation Test
Framework for GSoC 2026.

My relevant background:
I hold an R&D role at an institution where I research emerging tech,
build proof-of-concept systems, and run workshops — through this I've
worked hands-on with LLMs and built RAG pipelines end-to-end
(chunking, embedding, retrieval, re-ranking). Evaluating
non-deterministic AI system outputs is something I work with
regularly, which is directly relevant to this project.

On the engineering side:

• Backend intern at Shopflo — built production APIs in Spring Boot
  and Django
• Built and shipped Menukaze — a full-stack SaaS product
  (Node.js, React, PostgreSQL)
• Comfortable in TypeScript/Node.js, Python, React — the core stack
  of this project

My thinking on the approach:
The hardest part isn't writing 50 test scenarios — it's defining what
"success" means when an agent can solve the same task multiple ways.
I'd want to separate:

• Outcome correctness (did the file change as expected?)
• Tool efficiency (right tools used, or unnecessary steps taken?)
• Failure mode classification (wrong tool, hallucinated path, loop)

My questions:

1. Is defining "success criteria" part of the project scope, or is
   there an existing internal definition to build on?
2. For CI integration — preference to run evals on every PR or only
   scheduled builds given API costs?

Setting up the repo this week and planning an initial contribution
before submitting my proposal. Happy to share my draft early for
feedback.

Linkedin: https://www.linkedin.com/in/shathwik1/
GitHub: https://github.com/shathwik30

[aniruddhaadak80]

From my point of view, the most useful early contribution in this space is a clear task taxonomy plus outcome checks that are hard to game. Once debugging, review, and multi file tasks are represented well, maintainers can reason about real capability gaps instead of just aggregate pass rates. Efficiency and failure mode classification become much more valuable after that foundation exists, because then the metrics describe meaningful categories instead of a narrow slice of behavior.