# CODE_ENGINE_VAL_v2.0

Validation for the
Autonomous Engineer.

Engineering-grade datasets and benchmarks for models that manage entire repositories. We evaluate logic, planning, and execution across the full software development lifecycle.

50M+
SFT Tokens
100%
Verified Traces
GPT-4o
State of Art
SWE
Benchmark Ready

High-Fidelity Training.

We specialize in SFT and RLHF data that focuses on the nuances of programming—from function calling to secure coding standards.

SME VERIFIED

Logic & Planning

Rubric-aligned prompts that evaluate a model's ability to plan multi-step solutions before writing a single line of code.

# DATASET: PLANNING_V1

CoT Coding Traces

Human-verified Chain-of-Thought traces for complex debugging tasks, optimized for fine-tuning reasoning models.

# DATASET: REASONING_L3

API & Tool Use

Datasets focused on function calling and external tool interaction within secure, sandboxed environments.

# DATASET: TOOL_USE_v2

Technical
Benchmarks.

We go beyond simple snippets to test model performance in realistic, containerized development environments.

SWE-bench++

Evaluating agents on real GitHub issues with full repo context.

View Specs
Repo-QA

Measuring navigation accuracy across massive, unfamiliar codebases.

View Specs

Evaluation Deliverables

  • Execution Accuracy Report

    Verified pass@k metrics for real-world PR tasks.

  • Security Audit (Static/Dynamic)

    Detection of CWE-vulnerabilities in generated code.

  • Optimization Roadmap

    Instructions for improving SFT/RLHF reasoning densities.

Coding
Evaluation FAQ.

Understanding our methodology for secure, high-fidelity coding agent evaluation.

Acadify provides structured reasoning datasets for logic, chain-of-thought coding traces, and multi-file debugging tasks within containerized environments. These are designed to push the limits of repo-level agents.

SWE-bench++ is our proprietary enhancement of the industry standard, testing agents on real GitHub issues with 100% reproducible test harnesses and full repo context. We focus on high-fidelity environment reproduction.

Every dataset undergoes a 3-stage verification process: initial SME drafting, peer logic audit, and final deterministic execution testing (where code is compiled and tested against hidden unit tests).