How to Eliminate Requirement Bugs with AWS Kiro's Formal Logic Engine

What You Need

Before you start, ensure you have the following:

• AWS Account – with access to the Kiro agentic development platform and its Requirements Analysis feature.
• Clear Requirements Document – your software requirements written in natural language (e.g., user stories, functional specs).
• Basic Understanding of Formal Logic – not required, but helpful for interpreting the solver's outputs.
• Team Collaboration – involve developers, product managers, and testers to review and resolve flagged issues quickly.

Step-by-Step Guide

Step 1: Prepare Your Requirements in Natural Language

Write down your software requirements as you normally would—using plain English (or another natural language). Focus on completeness, but don't worry about ambiguity yet. The system will handle that later. For example, a requirement might read: "The user must be able to reset their password within 24 hours."

Tip: Group related requirements together. Avoid merging multiple conditions into one sentence, as this makes analysis harder.

Step 2: Input Requirements into Kiro's Requirements Analysis

Navigate to the Requirements Analysis module in AWS Kiro. Upload your requirements document or paste the text directly. The platform accepts standard formats like plain text, Markdown, or JSON. Click Analyze to begin the automatic process.

At this stage, nothing visible happens yet—the system is working in the background to transform your input.

Step 3: Let the LLM Rewrite Requirements into Testable Criteria

The first processing stage uses a large language model (LLM) to transform vague, natural-language statements into precise, testable criteria. For instance, "reset password within 24 hours" becomes "The system shall allow password reset only if the request is made within 1,440 minutes of the account creation or last password change."

You can review the LLM's output on the screen. It will appear as a structured list alongside your original text. If you see any misinterpretations, you can edit them manually before proceeding.

Step 4: Review the Formal Representation

The rewritten criteria are then translated automatically into formal mathematical logic—this is called the formal representation. AWS uses a variant of first-order logic. You don't need to read it fully, but scanning it helps understand how the system interprets your requirements. Look for logical operators like AND, OR, NOT, and quantifiers (∀, ∃).

If the formal representation seems incorrect, go back to Step 3 and refine the criteria. The system will regenerate the logic.

Step 5: Run Automated Reasoning with the SMT Solver

Click Run Formal Analysis. This triggers an SMT (Satisfiability Modulo Theories) solver—a 50-year-old logic engine that mathematically proves whether the requirements are consistent. The solver checks for:

• Contradictions – two rules that cannot both be true (e.g., "the user must be logged in" and "the user must be anonymous").
• Ambiguities – statements that can be interpreted in multiple conflicting ways.
• Gaps – missing constraints or undefined behaviors.
• Undefined behaviors – scenarios where no rule applies.

Processing usually takes a few seconds. When it finishes, a dashboard displays all found issues.

Step 6: Address Identified Issues

Each issue is presented as a plain-language question with two possible resolutions. For example: "Should password reset be allowed if the user's account is locked? (Yes/No)"

Choose the correct option based on your intended design. This typically takes 10–15 seconds per issue. The system automatically updates the formal representation and re-checks consistency. Continue until no more issues remain.

Tip: Involve domain experts (e.g., security, UX) when the question touches cross-functional requirements.

Step 7: Iterate and Finalize

After resolving all issues, export the cleaned requirements as a formal specification or back to natural language. Use this as the basis for coding and testing. Repeat the analysis whenever you add new requirements or modify existing ones.

AWS recommends running Requirements Analysis at the start of each sprint or milestone to catch bugs early.

Tips for Success

• Start Small – Test the process with a single module before scaling to your entire project.
• Combine with Human Review – While the SMT solver is mathematically sound, the LLM step can introduce errors. Always verify the initial rewrite.
• Use Consistent Terminology – Define key terms (e.g., "active user") in a glossary. The formal engine works best when terms have single meanings.
• Integrate into CI/CD – Automate requirement analysis as part of your deployment pipeline for continuous validation.
• Educate Your Team – Teach developers and product managers how to write requirements that are amenable to formal checking—use short, atomic sentences.
• Don't Overengineer – Not every requirement needs full formal verification. Focus on critical safety, security, or high-risk features.

By following these steps, you can dramatically reduce the 60% of bugs that originate from requirement errors—before a single line of code is written.