Blackfire Player is a PHP CLI application (blackfire-player) that executes declarative scenario files to crawl HTTP services, extract data, and assert responses, with integrated Blackfire performance profiling. It uses a custom DSL (`.bkf` files) to define sequences of steps (visit, click, submit, loop, while, etc.) and supports concurrent execution via PHP Fibers. The architecture is modular: a step hierarchy (AbstractStep, Step, etc.), dedicated step processors following the Chain of Responsibility pattern, an extension system for lifecycle hooks (before/after step, scenario), and a Symfony ExpressionLanguage wrapper providing scraping-specific functions (css, xpath, json, regex, fake, etc.). The tool integrates with the Blackfire SDK for profiling and assertion checks, and outputs results as JSON or text. It is distributed as a PHAR archive and a Docker image, built via GitHub Actions. The codebase is well-structured, heavily tested with PHPUnit, and follows modern PHP practices (typed properties, strict types, PHP 8.4+ features).
This repository provides the Bench2Drive benchmark for closed-loop end-to-end autonomous driving, accepted at NeurIPS 2024 Datasets and Benchmarks Track. It includes a large-scale multi-modal dataset (up to 7.3TB) collected in the CARLA simulator, an evaluation suite with metrics like Driving Score and Success Rate, and baseline results for over 10 methods. The codebase is a hybrid of research data pipeline and application: the dataset generation and evaluation scripts form the core data_research component, while the B2DVisualize Flask app, scenario runner, and leaderboard evaluator provide application-like interfaces for interaction and reproducibility. Data lineage: raw simulation data (sensor recordings, ground truth annotations) is collected via data_collect.py, transformed into compressed archives with JSON manifests, and evaluated using the leaderboard pipeline that computes multiple ability metrics. The repository answers research questions about the effectiveness of end-to-end driving policies under diverse urban scenarios.