Code Generation Models: Building a Reproducible Evaluation

A coding model generates responses. A coding agent adds tools, context selection, execution and iteration around that model. A ranking that mixes models, editors and agents without explaining those differences is difficult to reproduce.

This article provides an evaluation protocol. It does not claim that eight products were tested or publish invented success percentages.

Understand the benchmark boundary

SWE-bench evaluates repository-level issue resolution with an execution harness. A score belongs to the evaluated system and setup. It should not be transferred unchanged to a different agent, tool budget or dataset variant.

Even a reproduced benchmark result does not establish performance on your private repository. Dependency access, language, test quality and task definitions can differ substantially.

Define the task before running the agent

Choose a real defect with a reliable reproduction. Preserve a starting revision and write acceptance criteria independently of the model's proposed implementation.

For example, a cache invalidation task might require that an updated record becomes visible, unrelated cache entries remain valid, and failed writes do not publish new data. Those requirements create several useful checks beyond the happy path.

Record model identifier, agent version, prompt, tools, network access, time limit and retry budget. Check model facts at the source: GPT-5 and o1-pro are separate models, while Gemini 2.5 Pro has a documented input limit of 1,048,576 tokens.

Keep verification independent

Run the existing suite and additional checks that exercise the reported defect. Inspect changes to tests and build configuration. A patch that removes a failing assertion has not necessarily repaired the behavior.

Use held-out cases for edge conditions. Check error handling, permissions and concurrency where the task requires them. Do not demand irrelevant test volume merely to inflate coverage.

Retain unsuccessful attempts and the corrections needed from a reviewer.

Report results people can interpret

For each task, report whether the requirement was met, which checks ran, elapsed time, usage cost and review effort. Separate a tool failure from an incorrect patch, but count both in operational reliability.

If a model completes one task faster and another with fewer defects, present that tradeoff. An aggregate score can hide the category that matters most to the team.

A useful evaluation ends with a bounded conclusion: which configuration worked for which tasks under which conditions. It does not turn a green test command into a guarantee of secure, production-ready software.

Sardar Mukhshaf

Full-Stack & DevOps Engineer

I build scalable web applications, secure backend systems, and production-ready cloud infrastructure. My engineering focus spans interface design, API architecture, deployment pipelines, and observability. Follow for more deep dives into system architecture, performance engineering, and emerging technologies.

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