error-guessing

Employs error guessing to identify defects in complex logic and edge cases.

Updated Jul 5, 2026
One-click install
npx skills add https://github.com/phatnguyen975/functional-test-design --skill error-guessing
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: error-guessing
Source: https://github.com/phatnguyen975/functional-test-design/tree/main/error-guessing
Command: npx skills add https://github.com/phatnguyen975/functional-test-design --skill error-guessing

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill enhances systematic test design by applying expert-driven error guessing, identifying potential defects that might be missed by automated techniques.

Core Features & Use Cases

  • Supplement Systematic Testing: Use error guessing to find defects that systematic techniques may overlook.
  • Domain Knowledge & Experience: Incorporate tester's domain knowledge and historical defect data.
  • Structured Guessing: Apply the Fault Attack approach for structured guessing and improved coverage.
  • Test Case Prioritization: Prioritize test cases based on risk for efficient execution.
  • Use Case: After applying systematic testing techniques, use this Skill to explore potential defects in complex logic, boundary conditions, and multi-session effects.

Quick Start

/error-guessing --file="path/to/output.md"

Frequently Asked Questions about error-guessing

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I find software defects that systematic testing techniques miss?

Error guessing supplements systematic testing by applying a structured Fault Attack approach to identify potential defects. It leverages tester domain knowledge and historical defect data to explore complex logic, boundary conditions, and multi-session effects.

What is error guessing in software testing?

Error guessing is an expert-driven defect identification technique that supplements systematic test design. It uses historical defect data and domain knowledge to anticipate potential software failures in complex features and boundary conditions.

How do I prioritize test cases based on risk assessment?

You can prioritize test cases by applying structured error guessing to evaluate defect probability. This approach uses historical defect data and domain knowledge to target high-risk areas, ensuring efficient test execution for complex logic.

When should I use error guessing in my software testing workflow?

Use error guessing after applying systematic testing techniques to explore potential defects in complex logic, boundary conditions, and multi-session effects. It requires input from systematic testing and domain expertise to improve test coverage.

Can I use error guessing to identify boundary condition defects?

Yes, error guessing is optimized for identifying defects in boundary conditions, complex features, and multi-session effects. It applies structured guessing using historical defect data and domain knowledge to supplement systematic testing.