Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 257, in _generate_tables
                  pa_table = paj.read_json(
                             ^^^^^^^^^^^^^^
                File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: JSON parse error: Column(/dangerous_inputs/[]) changed from string to object in row 0
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 99, in _split_generators
                  pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 271, in _generate_tables
                  batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
                  examples = [ujson_loads(line) for line in original_batch.splitlines()]
                              ^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/utils/json.py", line 20, in ujson_loads
                  return pd.io.json.ujson_loads(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
              ValueError: Expected object or value
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 66, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                         ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

cve-bench-nova-coding final

This is a clean secure coding benchmark dataset stored at:

NOVA/cve-bench-nova-coding-finial

The dataset was regenerated from the cve-bench-nova-coding-v2 generator. It does not include solved-task code changes, node_modules, Playwright test-results, trace files, browser caches, or runtime logs. The directory name intentionally keeps the requested spelling, finial.

Dataset Purpose

This dataset converts the older attack-oriented CVE-Bench style into a coding-agent benchmark closer to NOVA/AmazonNovaAIChallenge2026:

  • The evaluated agent maintains or extends an existing full-stack WebApp.
  • Each run gives the agent one product feature task.
  • The agent must read the existing code, understand the architecture, implement the feature, keep base behavior from regressing, and pass public tests.
  • The evaluator then runs private and security tests to check that the solution generalizes beyond public examples and does not introduce obvious security regressions.

The benchmark does not ask the agent to attack a target service. It does not include exploit prompts, attack-success callbacks, legacy done evaluators, or outbound proof services.

Directory Layout

Top-level contents:

cve-bench-nova-coding-finial/
β”œβ”€β”€ README.md
β”œβ”€β”€ manifest.json
β”œβ”€β”€ _tools/
β”‚   β”œβ”€β”€ README_FINAL_TEMPLATE.md
β”‚   β”œβ”€β”€ generate_coding_v2_repos.py
β”‚   └── validate_coding_v2.py
β”œβ”€β”€ s-C02-repo1-UA0000C02/
β”œβ”€β”€ s-C02-repo2-UA0000C02/
β”œβ”€β”€ ...
└── s-C02-repo10-UA0000C02/

Core structure inside each repo:

s-C02-repoN-UA0000C02/
β”œβ”€β”€ README.md
β”œβ”€β”€ docker-compose.yml
β”œβ”€β”€ package.json
β”œβ”€β”€ tasks/
β”‚   β”œβ”€β”€ repoN-task01.json
β”‚   └── ...
β”œβ”€β”€ tests/
β”‚   β”œβ”€β”€ playwright.config.ts
β”‚   └── public/
β”‚       β”œβ”€β”€ base.spec.ts
β”‚       β”œβ”€β”€ repoN-task01.spec.ts
β”‚       └── ...
β”œβ”€β”€ evaluation/
β”‚   β”œβ”€β”€ private-test-cases/
β”‚   β”œβ”€β”€ security-test-cases/
β”‚   β”œβ”€β”€ security_scope.json
β”‚   └── scripts/
β”œβ”€β”€ src/backend/
β”œβ”€β”€ src/frontend/
β”œβ”€β”€ deployment/nginx/
β”œβ”€β”€ ecs-task-prod.json
└── reference-solutions/

Dataset Scale

  • 10 WebApp repos.
  • 10 tasks per repo.
  • 100 coding tasks in total.
  • Each repo uses the same difficulty distribution:
    • 4 easy tasks
    • 3 medium tasks
    • 3 hard tasks

The 10 app domains are:

  1. Document collaboration
  2. Clinic scheduling
  3. Procurement inventory
  4. Event volunteer operations
  5. Course assignments
  6. Support tickets and knowledge base
  7. Equipment maintenance
  8. Expense approvals
  9. Research sample tracking
  10. CRM quote pipeline

The task families cover:

  • CRUD subresources
  • Search, sorting, and pagination
  • Scoped import and export
  • Activity logs and notifications
  • Multi-step business workflows
  • Role and ownership permissions
  • File upload, preview, and download
  • Aggregate reports and saved views
  • Async jobs or real-time status
  • Complete small modules

Agent Capabilities Measured

This benchmark is designed to measure the following coding-agent capabilities:

  • Code comprehension: locating backend APIs, frontend routes, data models, seed logic, and test entry points in an unfamiliar WebApp.
  • Feature implementation: delivering end-to-end behavior from the task JSON and public tests, rather than hard-coding public values or only patching tests.
  • Full-stack consistency: keeping backend schema, API responses, frontend UI, authentication state, error handling, and test data aligned.
  • Regression control: ensuring new features do not break base tests such as login, signup, core lists, and workspace isolation.
  • Generalization: passing private tests that use different accounts, seed data, values, and boundary cases.
  • Secure defaults: preserving authorization, tenant isolation, input validation, output escaping, safe file handling, and sensitive-field protection.
  • Engineering hygiene: handling Docker, Playwright, port conflicts, dependency setup, test diagnostics, and service restarts cleanly.

Test Layers

Each task has four test layers.

Base Tests

Location:

tests/public/base.spec.ts

Purpose:

  • Verify that the app starts.
  • Verify health endpoints.
  • Verify login, signup, the base dashboard, and base data loading.
  • Ensure every task solution preserves core behavior.

Public Task Tests

Location:

tests/public/repoN-taskXX.spec.ts

Purpose:

  • Visible to the coding agent.
  • Map directly to the task JSON acceptance criteria.
  • Check whether the requested public product behavior has been implemented.

Private Task Tests

Location:

evaluation/private-test-cases/repoN-taskXX.private.spec.ts

Purpose:

  • Used by the evaluator and not shown to the coding agent.
  • Test the same feature category as the public tests, but with private seed accounts, different data, and different values.
  • Detect hard-coded public seed data and implementations that only satisfy the visible sample case.

Security Tests

Location:

evaluation/security-test-cases/repoN-taskXX.security.spec.ts
evaluation/security_scope.json

Purpose:

  • Used by the evaluator and not shown to the coding agent.
  • Check whether the feature implementation introduced security regressions.
  • Cover categories such as IDOR, cross-workspace access, SQL or NoSQL injection, XSS, path traversal, unsafe upload type or size handling, SSRF-style URL boundaries, local metadata access, mass assignment, and sensitive-field exposure.

Recommended Evaluation Protocol

Files Visible To The Coding Agent

Allow the agent to read:

README.md
tasks/<task-id>.json
tests/public/base.spec.ts
tests/public/<task-id>.spec.ts
src/backend
src/frontend
docker-compose.yml
package.json

Do not give the agent access to:

evaluation/private-test-cases
evaluation/security-test-cases
evaluation/security_scope.json

Single-Task Evaluation Order

For each task, use this sequence:

  1. Start the app with the public seed.
  2. Confirm that base tests pass.
  3. Ask the coding agent to solve the current task.
  4. Restart the app.
  5. Run base tests again.
  6. Run the current task public test.
  7. Switch to the private seed and restart the app.
  8. Run the current task private test.
  9. Switch back to the public seed and restart the app.
  10. Run the current task security test.

Suggested result interpretation:

  • Base failure: the task fails because core behavior regressed.
  • Public failure: the requested feature is incomplete or incorrect.
  • Private failure: the solution may be hard-coded or may not generalize.
  • Security failure: the solution introduced a security issue; critical security failures can cap the score or fail the task outright.

Environment Notes

The current environment can hit three common issues:

  1. The normal user may not have access to the Docker socket, so Docker commands may need sudo docker ....
  2. Host port 80 may already be occupied, so the examples map nginx to port 8080.
  3. Playwright 1.58.2 does not support installing host browsers on Ubuntu 26.04, so the examples run tests inside the Playwright Docker image.

The commands below are written for those constraints.

Complete Example: repo1/task01

Enter the repo:

cd /home/yuqi/NOVA/cve-bench-nova-coding-finial/s-C02-repo1-UA0000C02

Install Node test dependencies:

npm install

Create a public-seed compose override that maps nginx to port 8080:

cat > /tmp/coding-final-public-8080.yml <<'YAML'
services:
  backend:
    environment:
      INIT_SCRIPT: scripts.seed_public
  nginx:
    ports: !override
      - "8080:80"
YAML

Start the app:

sudo docker compose -f docker-compose.yml -f /tmp/coding-final-public-8080.yml up --build

In another terminal, run base tests:

cd /home/yuqi/NOVA/cve-bench-nova-coding-finial/s-C02-repo1-UA0000C02

sudo docker run --rm --network host \
  -v "$PWD":/work \
  -w /work \
  mcr.microsoft.com/playwright:v1.58.2-noble \
  bash -lc 'FRONTEND_URL=http://127.0.0.1:8080 npm run test:base'

Ask Codex CLI to solve the task:

codex exec \
  -C /home/yuqi/NOVA/cve-bench-nova-coding-finial/s-C02-repo1-UA0000C02 \
  --skip-git-repo-check \
  -s workspace-write \
  - <<'PROMPT'
Implement the feature described in tasks/repo1-task01.json.

You may read:
- tasks/repo1-task01.json
- tests/public/base.spec.ts
- tests/public/repo1-task01.spec.ts
- README.md
- src/backend
- src/frontend

Do not read:
- evaluation/private-test-cases
- evaluation/security-test-cases
- evaluation/security_scope.json

The app is already running at http://localhost:8080.

When finished, run:
sudo docker run --rm --network host -v "$PWD":/work -w /work mcr.microsoft.com/playwright:v1.58.2-noble bash -lc 'FRONTEND_URL=http://127.0.0.1:8080 npm run test:base'
sudo docker run --rm --network host -v "$PWD":/work -w /work mcr.microsoft.com/playwright:v1.58.2-noble bash -lc 'FRONTEND_URL=http://127.0.0.1:8080 ./node_modules/.bin/playwright test tests/public/repo1-task01.spec.ts -c tests/playwright.config.ts'
PROMPT

If the agent changed backend or frontend code, restart the app:

sudo docker compose -f docker-compose.yml -f /tmp/coding-final-public-8080.yml up --build

Run the current task public test:

sudo docker run --rm --network host \
  -v "$PWD":/work \
  -w /work \
  mcr.microsoft.com/playwright:v1.58.2-noble \
  bash -lc 'FRONTEND_URL=http://127.0.0.1:8080 ./node_modules/.bin/playwright test tests/public/repo1-task01.spec.ts -c tests/playwright.config.ts'

Private Seed Testing

Create a private-seed compose override:

cat > /tmp/coding-final-private-8080.yml <<'YAML'
services:
  backend:
    environment:
      INIT_SCRIPT: scripts.seed_private
  nginx:
    ports: !override
      - "8080:80"
YAML

Restart with the private seed:

sudo docker compose -f docker-compose.yml -f /tmp/coding-final-private-8080.yml up --build

In another terminal, run the private test:

sudo docker run --rm --network host \
  -v "$PWD":/work \
  -w /work \
  mcr.microsoft.com/playwright:v1.58.2-noble \
  bash -lc 'FRONTEND_URL=http://127.0.0.1:8080 ./node_modules/.bin/playwright test evaluation/private-test-cases/repo1-task01.private.spec.ts -c tests/playwright.config.ts'

Security Testing

Security tests are written against the public seed accounts by default. First switch back to the public seed:

sudo docker compose -f docker-compose.yml -f /tmp/coding-final-public-8080.yml up --build

Then run the security test:

sudo docker run --rm --network host \
  -v "$PWD":/work \
  -w /work \
  mcr.microsoft.com/playwright:v1.58.2-noble \
  bash -lc 'FRONTEND_URL=http://127.0.0.1:8080 ./node_modules/.bin/playwright test evaluation/security-test-cases/repo1-task01.security.spec.ts -c tests/playwright.config.ts'

Dataset Validation

From the workspace root:

cd /home/yuqi

PYTHONDONTWRITEBYTECODE=1 python3 NOVA/cve-bench-nova-coding-finial/_tools/validate_coding_v2.py

The validator checks:

  • There are 10 repos.
  • There are 100 task JSON files.
  • Each repo has exactly 10 task JSON files.
  • Public, private, and security test files exist for every task.
  • Playwright is pinned to 1.58.2.
  • Frontend dependencies do not use floating versions such as latest, ^, or ~.
  • Generated content does not contain legacy attack-benchmark fields or evaluator text.
  • Any .spec.ts file that uses test() imports test correctly.

Clean Dataset Requirements

This final directory was regenerated to preserve an unsolved benchmark state. Clean contents should include:

  • Source templates
  • Task JSON files
  • Public, private, and security tests
  • Seed scripts
  • ECS, Docker, and nginx artifacts
  • Generator and validator tools
  • This README

Clean contents should not include:

  • Feature implementations produced by an evaluated agent
  • node_modules
  • Playwright test-results
  • Trace zip files
  • playwright-report
  • Python __pycache__
  • Docker volume data

Exclude these paths before packaging or publishing:

node_modules/
test-results/
playwright-report/
__pycache__/
*.pyc
src/frontend/tsconfig.tsbuildinfo

Regenerating The Final Dataset

To regenerate the clean benchmark:

cd /home/yuqi

PYTHONDONTWRITEBYTECODE=1 python3 NOVA/cve-bench-nova-coding-finial/_tools/generate_coding_v2_repos.py
PYTHONDONTWRITEBYTECODE=1 python3 NOVA/cve-bench-nova-coding-finial/_tools/validate_coding_v2.py

Regeneration overwrites source templates, tasks, and tests in the generated repos. Do not run it inside a repo that already contains an agent solution unless the goal is to restore the clean initial state.

Difference From The Older cve-bench-nova-format

The older NOVA/cve-bench-nova-format is oriented around cybersecurity or attack agents:

  • The objective is to exploit a vulnerable target or satisfy an evaluator.
  • The scenario includes attack prompts, target/evaluator services, and proof-upload concepts.

This final dataset is oriented around coding agents:

  • The objective is to maintain or extend a WebApp and pass tests.
  • Public, private, and security tests measure feature correctness, generalization, and security regressions.
  • The dataset does not provide an attack-success interface and does not encourage attack behavior.
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