Good AI Task

AI compatibility

Writing pytest tests for pure math functions is a clean win for AI.

Good fit

AI can handle this.

Average across 2 submissions.

88
avg / 100

The honest read

Generating pytest unit tests for pure mathematical functions is one of the clearest automation wins available today. The task is structurally well-defined, the success criteria are verifiable by running the tests, and pure functions with no side effects are exactly what code agents handle best. The main risk is incomplete edge case coverage, which a human reviewer can catch quickly.

Aggregated across 2 submissions.

The five dimensions

Repeatability

High

Test generation for pure functions follows a consistent pattern: identify inputs, expected outputs, and edge cases. The structure is nearly identical across all 8 functions, making this highly repeatable.

Ambiguity Tolerance

High

Success criteria are crisp: tests must pass, cover edge cases, and exercise the documented behavior of each function. A code agent can verify correctness by running pytest and checking coverage.

Data & Tool Availability

High

The agent needs only the source module file and a Python environment with pytest installed — both are standard and easily provided. No external APIs or credentials are required.

Error Cost

Low

A flawed test suite is easily caught by running it or by a brief human review. No production system is affected, and the output is fully reversible — just regenerate or edit the tests.

Human Judgment Required

Low

Currency rounding and percentage logic have deterministic correct answers, so there is little room for subjective interpretation. A human reviewer should still sanity-check edge case selection, but the core work requires no special intuition.

What an agent would need

  • Access to the Python source module containing the 8 functions (file or code snippet)
  • Knowledge of the expected behavior and any documented rounding rules or precision requirements
  • A Python environment with pytest installed to validate that generated tests actually run
  • Specification of which edge cases matter (e.g., zero values, negative amounts, floating-point precision, currency boundary values)
  • Optional: existing test examples or a coverage threshold target to guide completeness

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  • Generate pytest unit tests for a Python module containing 8 pure functions that handle currency conversion, rounding, and percentage calculations. Cover edge cases.

    88