AI-driven refactoring and “Zero-Regression”: securing your code with AWS Kiro’s Spec-Driven Development

Refactoring legacy code is one of the most complex and costly bottlenecks in enterprise software maintenance. The advent of Generative Artificial Intelligence promised to solve the problem by translating and optimizing entire codebases in a matter of seconds. However, in real-world software engineering, simply speeding up code writing often introduces a new and lethal problem: the domino effect of architectural hallucinations.

This article analyzes the adoption of Spec-Driven Development (SDD) through AWS Kiro's native artifact-driven workflow. By leveraging rigorous requirements structures, technical design documents, and agent hooks, engineering teams can turn automated tests into the ultimate guardrail against regressions, while also creating a common language between non-technical stakeholders and software engineers.

1. Hallucination in legacy refactoring

When a traditional Large Language Model (LLM) is asked to "refactor a function to improve its performance and readability", the algorithm optimizes for stylistic probability and conciseness, not for semantic correctness or adherence to the business context.

The risk of "seemingly perfect" code

The main dangers of hallucination in refactoring activities are:

• Violation of implicit business rules: code changes that remove so-called "historical edge cases" (for example, null value handling that seems redundant but is tied to an old bug in an external database).
  
• Loss of numerical precision: replacing high-precision string conversions or custom math handlers with native floating-point types, in the name of cleaner syntax.
  
• Changes to invisible API contracts: renaming fields in JSON payloads or changing return types, breaking downstream consumers without raising any syntax error.
  
• Side effects and concurrency: "optimized" removal of in-memory locks or database transactions to reduce latency, introducing race conditions in high-load environments.

Example: naive refactoring of a financial function

Consider this legacy JavaScript/TypeScript module, lacking strict type annotations and modern async patterns:

// LEGACY CODE: Transaction handling with implicit dependencies 
// and missing types
function processPaymentLegacy(userId, amount, currency, retryCount = 0) {
	if (amount <= 0) {
        return { status: "FAILED", code: 400 };
    }

    // Historical bug: API v1 only accepts float strings formatted with 2     
      // decimal places
    const formattedAmount = Number(amount).toFixed(2);

    try {
        const res = legacyGatewayCall(userId, formattedAmount, currency);
        if (res.code === 200) {
            return { status: "SUCCESS", txId: res.txId };
        } else if (res.code === 503 && retryCount < 3) {
            return processPaymentLegacy(userId,amount,currency,retryCount+1);
        }
    } catch (e) {
        logError(e);
    }

    return { status: "FAILED", code: 500 };
}


A generic prompt sent to an unguided LLM often produces a TypeScript result like this: modern-looking, but “broken”:

/ HALLUCINATED REFACTORING (Modern TypeScript syntax, but logically broken)
export async function processPayment(
	userId: string, 
amount: number, 
currency: string): Promise<object> {

    if (amount <= 0) {
	    // BREAKS CONTRACT: Throws exception instead of 
        // returning a status object
        throw new Error("Invalid amount");     
    }

    
    // HALLUCINATION: Removes the 2-decimal string formatting required by   
    // legacy gateway
    const response = await legacyGatewayCall(userId, amount, currency);

    // HALLUCINATION: Completely removes the recursive retry logic 
    // for "cleanliness"
    if (response.code === 200) {
        return { status: "SUCCESS", txId: response.txId };
    }
    return { status: "FAILED", code: response.code };
}

2. Spec-Driven Development (SDD) according to Kiro

Continuing with the previous example, here is a valid set of business requirements written in EARS syntax:

The three core artifacts of Kiro's SDD

1. requirements.md (the "what" and the "why"): this is the document where non-technical stakeholders and product managers define business rules and acceptance criteria. Kiro encourages the use of EARS syntax (Easy Approach to Requirements Syntax) to maintain the necessary rigor (for example, WHEN [trigger] THE SYSTEM SHALL [response]).
2. design.md (the architecture): before generating any code, Kiro translates the requirements into a technical design document describing component hierarchies, API payloads, and data flow. Developers review it to ensure its structural soundness.
3. tasks.md (the execution plan): Kiro breaks the design down into granular, step-by-step tasks. The agent executes them in sequence, and the completion of each task can trigger automated tests that verify its outcome.

Example: requirements in EARS syntax

# Legacy Payment Processing Requirements

1. **Precision Constraint:**
   WHERE a transaction is initiated, THE SYSTEM SHALL format the payment amount as a string with exactly two decimal places before transmitting it to the legacy gateway.

2. **Resilience Strategy:**
   WHEN the legacy gateway returns a 503 HTTP status code, THE SYSTEM SHALL retry the request up to 3 times.

3. **Contract Adherence:**
   IF the transaction fails after all retries, THE SYSTEM SHALL return a JSON dictionary containing `{"status": "FAILED"}` and SHALL NOT raise an unhandled exception.

3. AWS Kiro architecture and the Zero-Regression workflow

AWS Kiro operates through a test-driven Agentic Execution Loop, tightly coupled to the tasks.md file and to the repository's automated tests.

Kiro picks up a refactoring task, applies the code changes, and then runs an Agent Hook. If the hook (which launches the test suite) fails, Kiro captures the stack trace, feeds it back into its own context window, and attempts to fix the hallucination or logical error. It only moves on to the next task once the tests pass.

4. Native Kiro configuration: steering rules and agent hooks

To enable the Zero-Regression approach, you need to configure Kiro's operational boundaries through its native directory / file structure within the repository. Below is a breakdown of the required directories and files.

Repository structure

my-enterprise-service/
├── .kiro/
│   ├── steering/              # Steering Rules
│   │   ├── architecture.md
|   |   ├── design.md
|   |   └── requirements.md
│   ├── hooks/                 # Agent Hooks
│       └── post-task.json
|   └── specs/                 # Spec Tasks
│       └── tasks.md
├── defer" defer" defer" src/
│   └── paymentService.ts
└── tests/
    └── payment-service.spec.ts

1. Steering Rules (.kiro/steering/architecture.md)

Steering rules are markdown files that define constraints applying to the entire team. Kiro ingests them and applies them globally to every task.

# Architectural Steering Rules

1. **Immutability**: All domain data structures and DTOs must be immutable.

2. **Exceptions**: Do not throw unhandled exceptions from service layers. Always respect the HTTP `Result` envelope pattern.

3. **Logging**: Use `@aws-lambda-powertools/logger`. Never log clear-text PAN or PII data.

2. Business requirements (.kiro/steering/requirements.md)

Continuing with the previous example, here is a valid set of business requirements written in EARS syntax:

# Legacy Payment Processing Requirements

1. **Precision Constraint:**
   WHERE a transaction is initiated, THE SYSTEM SHALL format the payment amount as a string with exactly two decimal places before transmitting it to the legacy gateway.

2. **Resilience Strategy:**
   WHEN the legacy gateway returns a 503 HTTP status code, THE SYSTEM SHALL retry the request up to 3 times.

3. **Contract Adherence:**
   IF the transaction fails after all retries, THE SYSTEM SHALL return a JSON dictionary containing `{"status": "FAILED"}` and SHALL NOT raise an unhandled exception.

3. Technical design (.kiro/steering/design.md)

After reviewing requirements.md, Kiro generates (or receives ready-made) design.md to map out the technical breakdown of components, interfaces, and error handling before touching any source file:

# Design Specification: Payment Service Refactoring

## 1. Architecture Overview
Refactor the legacy `processPaymentLegacy` function into an asynchronous, strongly-typed TypeScript module (`processPayment`) adhering to `@aws-lambda-powertools/logger`.

## 2. Interface Contracts
```typescript
export interface PaymentRequest {
  readonly userId: string;
  readonly amount: number;
  readonly currency: string;
}

export interface PaymentResponse {
  readonly status: "SUCCESS" | "FAILED";
  readonly txId?: string;
  readonly errorCode?: number;
}

4. Data flow and retry strategy (.kiro/specs/task.md)

# Implementation Plan

## Overview

Refactor the legacy payment processing code into a strongly-typed, async TypeScript module that satisfies the precision, resilience, and contract-adherence requirements.

## Tasks

- [ ] 1. Create TypeScript Types & Interfaces
  - Define `PaymentRequest` and `PaymentResponse` inside `src/types.ts`.
  - Enforce immutability using `readonly` modifiers per `.kiro/steering/architecture.md`.

- [ ] 2. Refactor Payment Execution Module
  - Refactor `src/payment-service.ts` to implement the async `processPayment` function.
  - Implement two-decimal string formatting constraint for amount payloads.
  - Implement 503 retry resilience logic.

- [ ] 3. Run Post-Task Verification Hook
  - Execute `.kiro/hooks/post-task.json` to ensure `tests/payment-service.spec.ts` passes.

## Task Dependency Graph

```json
{
  "waves": [
    {
      "wave": 1,
      "tasks": [1]
    },
    {
      "wave": 2,
      "tasks": [2],
      "dependsOn": [1]
    },
    {
      "wave": 3,
      "tasks": [3],
      "dependsOn": [2]
    }
  ]
}
```

## Notes

- Task 2 depends on the interfaces defined in Task 1; complete them in order.

- Task 3 is a verification step and should only run after Task 2 is complete.

- All domain structures must use `readonly` modifiers (immutability rule).

- Do not throw unhandled exceptions from the service layer; return `{"status": "FAILED"}` on exhausted retries.

- Never log clear-text PAN or PII data.

5. Agent Hook (.kiro/hooks/post-task.json)

Agent hooks are scripts executed in response to specific lifecycle events. The post-task hook is crucial for Zero-Regression: it automatically runs the test suite as soon as Kiro considers a refactoring step complete.

{
  "version": "v1",
  "hooks": [
    {
      "name": "Post-Task Test Verification",
      "trigger": "PostTaskExec",
      "description": "Runs the Jest test suite after each spec task completes. Exits with code 1 on failure so Kiro can detect regression and self-correct.",
      "action": {
        "type": "command",
        "command": "cd \"/Users/alessandrogaggia/Projects/Kiro Article\" && npm test -- --json --outputFile=test-results.json; if [ $? -ne 0 ]; then echo \"Tests failed. Analyze test-results.json and correct the code.\"; exit 1; fi; echo \"Tests passed. Zero regression verified.\""
      }
    }
  ]
}

5. Practical walkthrough: refactoring with Kiro's native artifacts

Let's walk through the refactoring of our legacy payment module using Kiro's SDD.
First, a developer writes a rigorous test based on requirements.md:

// tests/payment.spec.ts
import { processPayment } from '../src/paymentService';


describe('Payment Service Contract', () => {
  
it('should guarantee exact float precision and retry behavior', async () => {
    
// Mock temporary 503 Gateway failure
    
jest.spyOn(global, 'fetch')
      
.mockResolvedValueOnce(new Response(null, { status: 503 }))
      
.mockResolvedValueOnce(new Response(JSON.stringify({ txId: "TX-123" }), { status: 200 }));

 
   
const result = await processPayment({ userId: "USR-1", amount: 19.99, currency: "EUR" });


 
expect(result.status).toBe("SUCCESS");
    
expect(fetch).toHaveBeenCalledTimes(2); // Verifies retry occurred
  
   });

});

Now we are ready to launch Kiro. There are several ways to ask it to start working on the task list.

Method 1: Kiro Spec Panel (UI button)

1. Open the Spec Panel in the Kiro IDE sidebar.
2. Navigate to tasks.md.
3. Click the "Play / Start Task" icon (⚡) next to Task 1.

Method 2: command in the Kiro chat (context mention)

In the Kiro chat sidebar, reference the task file directly using @tasks.md:

@tasks.md Execute Task 1

or

Start working on Task 1 from tasks.md following our steering rules.

Method 3: Kiro autonomous agent mode (Autopilot)

1. Switch Kiro from Supervised to Autopilot mode in the bottom status bar.
2. Kiro autonomously picks up the tasks in sequence from tasks.md:
   • It reads Tasks 1, 2 and 3.
   • It generates the code in defer" defer" src/types and defer" defer" src/paymentService.ts.
   • It launches .kiro/hooks/post-task.sh to run tests/payment.spec.ts.
   • If the tests pass, Kiro ticks the task checkbox (- [x] Task 1) in tasks.md and moves on to the next task or generates a PR.
   • If the tests fail, Kiro reads the error stack trace from test-results.json and enters the Agentic Self-Correction Loop.

When Kiro executes the tasks defined in tasks.md, the file shows the agentic loop in action:

After each task, it attempts to run the tests, checks their structure, and fixes it when necessary:


Once all changes to specs and data are complete, it runs the required hook:


Once all tasks are completed, here is the validated, refactored code:

// defer" defer" src/types.ts (Added per task's request)

/**
 * Domain types for the payment service.
 * All fields are readonly to enforce immutability per architecture rules.
 */

export interface PaymentRequest {
  readonly userId: string;
  readonly amount: number;
  readonly currency: string;
}

export interface PaymentResponse {
  readonly status: "SUCCESS" | "FAILED";
  readonly txId?: string;
  readonly code?: number;
}


// defer" defer" src/paymentService.ts (Refactored by Kiro & Validated by Hooks)
// Refactored payment service
// - Async, strongly-typed, fetch-based
// - Amount formatted to 2 decimal places (requirements.md §1)
// - 503 retry up to 3 times (requirements.md §2)
// - Never throws; always returns Result envelope (requirements.md §3, architecture.md §2)
// - No PAN/PII logged (architecture.md §3)

import { Logger } from "@aws-lambda-powertools/logger";
import { PaymentRequest, PaymentResponse } from "./types";

const logger = new Logger({ serviceName: "payment-service" });

const LEGACY_GATEWAY_URL =
  process.env.LEGACY_GATEWAY_URL ?? "https://gateway.internal/v1/charge";
const MAX_RETRIES = 3;

export async function processPayment(
  request: PaymentRequest
): Promise<PaymentResponse> {
  return executeWithRetry(request, 0);
}

async function executeWithRetry(
  request: PaymentRequest,
  attempt: number
): Promise<PaymentResponse> {
  // Requirement §1: format amount as string with exactly 2 decimal places
  const formattedAmount = request.amount.toFixed(2);

  try {
    const response = await fetch(LEGACY_GATEWAY_URL, {
      method: "POST",
      headers: { "Content-Type": "application/json" },
      body: JSON.stringify({
        userId: request.userId,
        amount: formattedAmount,
        currency: request.currency,
      }),
    });

    if (response.ok) {
      const body = (await response.json()) as { txId?: string };
      return { status: "SUCCESS", txId: body.txId };
    }

    // Requirement §2: retry on 503 up to MAX_RETRIES times
    if (response.status === 503 && attempt < MAX_RETRIES) {
      logger.warn("Gateway returned 503, retrying", {
        attempt,
        currency: request.currency,
      });
      return executeWithRetry(request, attempt + 1);
    }

    // All retries exhausted or non-retryable failure — requirement §3
    logger.error("Gateway call failed, returning FAILED", {
      statusCode: response.status,
      attempt,
      currency: request.currency,
    });
    return { status: "FAILED", code: response.status };
  } catch (error) {
    // Architecture rule §2: never propagate unhandled exceptions from service layer
    // Architecture rule §3: log error reference only, no PII
    logger.error("[payment-service] Gateway call threw an error", {
      attempt,
      currency: request.currency,
    });
    return { status: "FAILED", code: 500 };
  }
}

6. AWS enterprise architecture: a continuous guardrailing pipeline

To ensure that the Kiro integration works at enterprise scale, the execution environment must be integrated with native AWS services for test isolation and security. A common example might be:

1. AWS CodePipeline: intercepts refactoring requests opened by developers or scheduled based on accumulated technical debt.
2. AWS ECS Fargate (ephemeral sandbox): provides an isolated, secure environment where the Kiro agent and its post-task hooks can run arbitrary code and tests without the risk of contaminating staging or production infrastructure.
3. Amazon Bedrock and Kiro Core: processes agentic requests while keeping data within the corporate VPC, ensuring that confidential source code is not used to train public models.
4. Amazon CloudWatch and Amazon CloudWatch Logs: tracks every agent iteration, failed hook logs, and applied fixes, leaving a complete audit trail for compliance and security reviews.

7. Comparison matrix: refactoring methodologies compared

Here is a simple table comparing traditional refactoring, refactoring driven by generic prompts, and SDD refactoring with Kiro:

8. A guide for Tech Leads: implementing the Human Review Gate

Despite Kiro's advanced automation, the Tech Lead's role remains central to system security. "Zero-Regression" does not eliminate human review, but it radically changes its nature:

• From "bug hunting" to "artifact review": the reviewer no longer checks the code logic line by line. Instead, they focus on ensuring that requirements.md faithfully reflects business needs and that the generated design.md is structurally sound.
• Rule enforcement: Tech Leads govern the .kiro/rules/ directory, continuously updating architectural constraints as the system evolves.
• Security gate approval: reviewing automatically generated PRs knowing that the post-task hooks have already proven that the code passes all regression tests and complies with the specified design.

By adopting this workflow, the Lead Dev shifts from bottleneck to supervisor of an agentic process driven by objective data in markdown format.

TL;DR

• The risk of generic AI: traditional AI models produce stylistic hallucinations during refactoring: the code looks clean but silently breaks legacy business rules and API contracts.
  
• Native Spec-Driven Development (SDD): Kiro solves the problem by abandoning generic prompts in favor of rigorous artifact hierarchies (requirements.md, design.md and tasks.md) that define exact behaviors using EARS syntax.
  
• Steering Rules and Agent Hooks: developers govern the AI using .kiro/steering/ to set architectural constraints and .kiro/hooks/ to run test suites immediately after each task is executed.
  
• Guaranteed Zero-Regression approach: Kiro leverages an Agentic Self-Correction Loop. If a post-task hook fails, Kiro analyzes the error and fixes the code autonomously before creating the Pull Request, ensuring PRs comply with business contracts.

Conclusion

Adopting AI for refactoring shouldn't mean living in constant fear of subtle regressions. By embracing Spec-Driven Development with Kiro, executable artifacts (requirements.md, design.md, tasks.md) and automated post-task hooks become immutable guardrails. This elevates generative AI from a chaotic completion tool to a disciplined, self-correcting engineering partner, allowing teams to eliminate technical debt while ensuring a Zero-Regression approach.