# CODE_ENGINE_VAL_v2.0

Validation for the
Autonomous Engineer.

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

Request API Access Technical Specs
acadify_eval_engine v2.4
// Init Sandboxed Environment: SWE-bench++ await acadify.eval.start({ "repo": "django/django", "issue": "#14672", "agent_url": "https://api.your-model.com/v1/chat" }); > Agent cloning repository... [OK] > Agent analyzing 14,203 files... [OK] > Agent applying patch diff... [OK] // Executing Deterministic Test Harness const results = await acadify.eval.runTests(); > Pass@1: 100% (All unit tests verified)
50M+
SFT Tokens
100%
Verified Traces
GPT-4o
State of Art
SWE
Benchmark Ready
TRAINING DATASETS

High-Fidelity Training Data.

We specialize in SFT (Supervised Fine-Tuning) and RLHF (Reinforcement Learning from Human Feedback) data that focuses on the extreme nuances of programming—from advanced function calling to secure coding standards across multi-file repositories.

Logic & Architecture Planning

Rubric-aligned prompts that evaluate a model's ability to plan multi-step solutions, architect scalable systems, and map out file dependencies before writing a single line of executable code.

# DATASET: PLANNING_V1

Chain-of-Thought Traces

Human-verified Chain-of-Thought (CoT) execution traces for complex debugging tasks. These datasets are hyper-optimized for fine-tuning reasoning models to self-correct during generation.

# DATASET: REASONING_L3

API & External Tool Use

Datasets focused exclusively on function calling, CLI execution, and external tool interaction within secure, dockerized sandboxes to teach agents how to use external environments.

# DATASET: TOOL_USE_v2
EVALUATION FRAMEWORKS

Technical Benchmarks.

We go significantly beyond simple LeetCode snippets to test model performance in realistic, containerized, multi-container development environments.

SWE-bench++

Evaluating autonomous agents on real GitHub issues with full repo context and deterministic grading.

View Protocol Specs
Repo-QA Benchmark

Measuring spatial navigation and context retrieval accuracy across massive, unfamiliar codebases.

View Protocol Specs

Enterprise Evaluation Deliverables

  • Execution Accuracy Report

    Verified pass@k metrics for real-world PR tasks, showcasing exact success rates on live codebases.

  • Security Audit (Static/Dynamic)

    Deep-dive detection of CWE-vulnerabilities, injection flaws, and memory leaks in the model-generated code.

  • Optimization Roadmap

    Actionable instructions and feedback loops for improving SFT/RLHF reasoning densities in your next training run.

SUPPORT

Evaluation FAQ.

Understanding our engineering 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 specifically designed to push the limits of repo-level agents rather than simple completion models.

SWE-bench++ is our proprietary enhancement of the industry standard. It tests autonomous agents on real, historical GitHub issues with 100% reproducible test harnesses and full repository context. We focus heavily on high-fidelity environment reproduction and removing flaky tests.

Every single dataset undergoes a rigorous 3-stage verification process: initial SME drafting by senior engineers, peer logic audits, and final deterministic execution testing where the generated code is compiled and tested against hidden unit test suites.

Yes. All evaluations and agent benchmarking tasks are executed in highly secure, ephemeral dockerized sandboxes that are destroyed immediately after trace generation to ensure 100% data privacy and enterprise security.

Ready to benchmark your models?

Get immediate access to our engineering-grade evaluation frameworks and validation APIs.

Request API Access