@autobe/agent
Version:
AI backend server code generator
162 lines (154 loc) • 6.4 kB
text/typescript
// import { tags } from "typia";
export interface IAutoBeTestWriteApplication {
/**
* Main entry point for AI Function Call - generates complete E2E test code.
*
* The AI executes this function to perform the entire test generation
* workflow: scenario analysis → draft implementation → code review → final
* code production. This structured approach ensures high-quality,
* compilation-error-free test code.
*
* @param props Complete specification for test generation including scenario,
* domain, and implementation steps
*/
write(props: IAutoBeTestWriteApplication.IProps): void;
}
export namespace IAutoBeTestWriteApplication {
export interface IProps {
/**
* Step 1: Strategic test planning and scenario analysis.
*
* AI analyzes the given test scenario and creates a comprehensive
* implementation strategy. This planning phase is crucial for generating
* well-structured, maintainable test code. The AI must define test
* methodology, data preparation, execution flow, and validation logic
* before proceeding to code implementation.
*
* Workflow: Input scenario → Strategic analysis → Detailed test plan
*/
scenario: string;
/**
* Step 2: Functional domain classification for test organization.
*
* AI determines the appropriate domain category based on the scenario
* analysis. This classification drives file structure, test categorization,
* and logical grouping. The domain must be a single, lowercase word in
* snake_case format that represents the primary API resource.
*
* Workflow: Scenario analysis → Domain identification → Test organization
* structure
*/
domain: string;
/**
* Step 3: Initial TypeScript E2E test code implementation.
*
* AI generates the first working version of the test code based on the
* strategic plan. This draft must be compilation-error-free and follow
*
* @nestia/e2e framework conventions. The code should implement all planned
* test scenarios with proper async/await patterns, type safety, and
* comprehensive error handling.
*
* Workflow: Strategic plan → TypeScript implementation → Functional test
* code
*
* Critical: NO import statements, start directly with 'export async function'
*/
draft: string;
/**
* Steps 4-5: Code review and final refinement process.
*
* Contains the iterative improvement workflow that transforms the initial
* draft into production-ready test code. The review phase identifies issues
* to fix or code to delete, followed by the final phase that produces the
* polished, production-ready test implementation.
*
* Workflow: Draft → Review analysis → Final implementation
*/
revise: IReviseProps;
}
export interface IReviseProps {
/**
* Step 4: Code review and quality assessment.
*
* **🚨 TWO TYPES OF REVISIONS: FIX AND DELETE 🚨**
*
* AI performs a thorough review of the draft implementation for:
*
* **1. FIX - Improve existing code:**
*
* **Compilation & Syntax:**
*
* - TypeScript compilation errors and type mismatches
* - Syntax errors and missing semicolons/brackets
* - Correct function signatures and parameter types
*
* **Framework Compliance:**
*
* - @nestia/e2e framework conventions adherence
* - Proper API SDK function calling patterns
* - Correct use of typia.assert() and TestValidator functions
*
* **Business Logic & Test Coverage:**
*
* - Complete workflow implementation (authentication → data setup → main test
* → validation)
* - Realistic business scenarios and user journeys
* - Edge case handling and error condition testing
* - Proper data dependencies and cleanup procedures
*
* **2. DELETE - Remove prohibited code entirely:**
*
* **🚨 TYPE ERROR TESTING - DELETE IMMEDIATELY 🚨**
*
* - DELETE any code using `as any` to send wrong types
* - DELETE any intentional type mismatches for "testing"
* - DELETE any missing required fields testing
* - DELETE tests that contradict compilation requirements
*
* **Code Quality & Security:**
*
* - Type safety violations (any, @ts-ignore, etc.) - DELETE if found
* - Variable naming and code organization - FIX if needed
* - Performance considerations and resource management
* - Security best practices in test data generation
*
* Workflow: Draft code → Systematic analysis → FIX or DELETE decisions
*
* The review must identify concrete issues with line-by-line feedback and
* provide actionable solutions (FIX) or deletion instructions (DELETE) for
* each problem discovered.
*
* **DO NOT FIX TYPE ERROR TESTS - DELETE THEM COMPLETELY**
*/
review: string;
/**
* Step 5: Final production-ready test code.
*
* AI produces the final, polished version of the test code incorporating
* all review feedback. This code represents the completed test
* implementation, ready for production deployment. When the draft code is
* already perfect with no issues found during review, this value can be
* null, indicating no revisions were necessary.
*
* **🚨 CRITICAL: APPLY ALL FIXES AND DELETIONS FROM REVIEW 🚨**
*
* - FIX all correctable issues identified in review
* - DELETE all prohibited code identified in review
* - If review found type error tests, they MUST be deleted in final
* - If review found code to DELETE, final MUST be different from draft
* - If review finds NO issues requiring changes, set to null
*
* All identified issues must be resolved, and the code must meet the
* highest quality standards. A null value indicates the draft code already
* meets all requirements without modification.
*
* Workflow: Review feedback → Apply FIXES → Apply DELETIONS →
* Production-ready implementation (or null if no changes needed)
*
* This is the ultimate deliverable that will be used in the actual test
* suite when provided, otherwise the draft is used as-is.
*/
final: string | null;
}
}