behave-pool

Parallel test execution for Behave BDD via native ITestRunner. Workers run in isolated processes with spawn start method for clean interpreter state on every platform.

Features

• Native ITestRunner — Registered via --runner= or behave.ini. Zero monkey-patching.
• Process isolation — spawn start method ensures clean state in every worker, on every OS.
• Dynamic dispatch — multiprocessing.Process + Queue. Workers consume work units as they finish.
• @serial tag — Non-parallelizable scenarios run sequentially after the parallel phase.
• LPT load balancing — Historical durations for optimal work distribution.
• Timing persistence — .behave-pool-timing.json stores durations between runs.
• Unified JSON report — Merges all worker reports into a single behave-modern-json-report ExecutionReport (schema v1.1.0) with statistics, environment info, and full feature/scenario/step details.
• Ecosystem integration — Optional behave-priority, behave-modern-json-report. The unified report is directly consumable by any tool in the ecosystem.
• Zero heavy dependencies — Only stdlib multiprocessing + behave>=1.3.0.

Installation

pip install behave-pool

Quick start

1. Register the runner in your behave.ini:

   [behave.runners]
   parallel = behave_pool:ParallelRunner

2. Run Behave with parallel workers:

   behave --runner=parallel --parallel 4 --parallel-scheme feature features/

How it works

┌─────────────────────────────────────────────────┐
│                  ParallelRunner                  │
│                                                  │
│  1. Plan    — parse features, create work units  │
│  2. Split   — separate @serial from parallel     │
│  3. Dispatch — N workers consume from queue      │
│  4. Collect — gather results, update timings     │
│  5. Serial  — run @serial units one at a time    │
└─────────────────────────────────────────────────┘
         │                          │
    ┌────▼────┐               ┌────▼────┐
    │ Worker 0 │               │ Worker N │
    │ (spawn)  │    ...        │ (spawn)  │
    │          │               │          │
    │ parse    │               │ parse    │
    │ features │               │ features │
    │ run      │               │ run      │
    │ report   │               │ report   │
    └──────────┘               └──────────┘

Each worker runs in an isolated process with the spawn start method, guaranteeing a clean interpreter state regardless of OS or Python version. Workers consume work units from a shared JoinableQueue and write WorkerResult objects back to a result queue. The coordinator collects results, persists timings, and returns the aggregated exit code.

CLI options

Option	Default	Description
--parallel N	1	Number of worker processes. 1 = sequential passthrough.
--parallel-scheme	feature	Parallelization unit: feature (scenario planned for future).
--parallel-balance	lpt	Work ordering: lpt (longest first) or fifo (insertion order).
--parallel-timing-file	.behave-pool-timing.json	Path to timing file for LPT balancing.
--parallel-report	behave-pool-report.json	Path to unified JSON report (behave-modern-json-report format).

Usage

Feature-level parallelization

Each feature file runs in its own worker process. Workers are dispatched dynamically and consume work units from a shared queue.

# 4 worker processes, LPT balancing
behave --runner=parallel --parallel 4 features/

Serial scenarios

Tag scenarios with @serial to run them sequentially after all parallel work units complete:

@serial
Scenario: Database migration
  Given the database is empty
  When I run the migration
  Then all tables should exist

LPT load balancing

By default, behave-pool uses Longest Processing Time (LPT) scheduling. It stores historical durations in .behave-pool-timing.json and dispatches the slowest features first, minimizing total wall-clock time.

# Use FIFO ordering instead of LPT
behave --runner=parallel --parallel 4 --parallel-balance fifo features/

Unified JSON report

After all workers finish, behave-pool merges their results into a single JSON report in the behave-modern-json-report ExecutionReport format (schema v1.1.0). This report includes:

• Execution metadata — unique ID, status, duration, timestamps.
• Aggregate statistics — feature/scenario/step counts, pass rate, error count, per-tag breakdown.
• Environment info — Python and Behave versions, OS, CI provider, git branch/commit.
• Full feature tree — features, scenarios, and steps with IDs, locations, durations, errors, and tracebacks.

# Default report path
behave --runner=parallel --parallel 4 features/
# → writes behave-pool-report.json

# Custom report path
behave --runner=parallel --parallel 4 \
    --parallel-report reports/run.json \
    features/

Any tool built for the behave-modern-json-report ecosystem (HTML formatters, dashboards, AI analyzers) can consume the parallel report directly — no conversion needed.

behave.ini configuration

All CLI options can also be set in behave.ini:

[behave]
parallel = 4
parallel-scheme = feature
parallel-balance = lpt
parallel-timing-file = .behave-pool-timing.json
parallel-report = behave-pool-report.json

Requirements

• Python >=3.11
• behave >=1.3.0

Example

A complete working example is included in examples/calculator/. It demonstrates parallel execution, @serial scenarios, and the unified JSON report:

cd examples/calculator
behave --runner=parallel --parallel 4
# → runs 3 scenarios (2 parallel + 1 @serial)
# → writes behave-pool-report.json with ExecutionReport format

Documentation

Full documentation is available at https://mathiaspaulenko.github.io/behave-pool/.

Contributing

Contributions are welcome! See CONTRIBUTING.md for setup instructions and guidelines.

Please review our Code of Conduct before participating.

Changelog

See CHANGELOG.md for notable changes.

License

MIT — Copyright (c) 2026 Mathias Paulenko

Acknowledgements

• Behave — the BDD framework this library extends.
• Contributor Covenant — Code of Conduct.
• Keep a Changelog — Changelog format.