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pypipkg:pypi/crosshair-tool@0.0.x
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tessl/pypi-crosshair-tool

tessl install tessl/pypi-crosshair-tool@0.0.0

Analyze Python code for correctness using symbolic execution and SMT solving to automatically find counterexamples for functions with type annotations and contracts.

Agent Success

Agent success rate when using this tile

86%

Improvement

Agent success rate improvement when using this tile compared to baseline

1.25x

Baseline

Agent success rate without this tile

69%

task.mdevals/scenario-1/

Configuration Validator

Build a system that uses symbolic execution to find valid configuration parameters that satisfy system constraints.

Problem Description

You need to implement configuration validation functions for a server deployment system. The system must find valid configuration values that satisfy multiple interdependent constraints.

Requirements

Implement a function validate_server_config that verifies a server configuration is valid. The configuration has the following parameters:

  • worker_threads: Number of worker threads (must be positive integer)
  • connection_pool_size: Size of connection pool (must be positive integer)
  • max_memory_mb: Maximum memory in MB (must be positive integer)
  • timeout_seconds: Request timeout in seconds (must be positive integer)

The configuration must satisfy these constraints:

  1. worker_threads must be between 1 and 32
  2. connection_pool_size must be at least worker_threads * 2
  3. max_memory_mb must be at least connection_pool_size * 10
  4. timeout_seconds must be between 1 and 300
  5. The total score (worker_threads * 100 + connection_pool_size * 10) must not exceed 5000

The function should raise a ValueError with a descriptive message if any constraint is violated.

Implement a second function find_optimal_config that uses symbolic execution to find a configuration that maximizes worker_threads while satisfying all constraints, given a fixed max_memory_mb budget.

Test Cases

  • Configuration with worker_threads=4, connection_pool_size=8, max_memory_mb=80, timeout_seconds=30 is valid @test
  • Configuration with worker_threads=4, connection_pool_size=6, max_memory_mb=80, timeout_seconds=30 raises ValueError because pool size is too small @test
  • Finding optimal config with max_memory_mb=500 returns a valid configuration that maximizes worker threads @test

Implementation

@generates

API

from typing import NamedTuple

class ServerConfig(NamedTuple):
    """Server configuration parameters."""
    worker_threads: int
    connection_pool_size: int
    max_memory_mb: int
    timeout_seconds: int

def validate_server_config(config: ServerConfig) -> None:
    """
    Validate a server configuration against system constraints.

    Raises:
        ValueError: If any constraint is violated, with a message describing the issue.
    """
    pass

def find_optimal_config(max_memory_mb: int) -> ServerConfig:
    """
    Find a valid configuration that maximizes worker_threads given a memory budget.

    Uses symbolic execution to search the constraint space.

    Args:
        max_memory_mb: The memory budget constraint

    Returns:
        An optimal ServerConfig that maximizes worker_threads

    Raises:
        ValueError: If no valid configuration exists for the given budget
    """
    pass

Dependencies { .dependencies }

crosshair-tool { .dependency }

Provides symbolic execution and constraint solving capabilities for searching valid parameter combinations.

@satisfied-by