Timestamp when the event was created.
ISO 8601 formatted date-time string indicating when this event was emitted by the system. This timestamp is crucial for event ordering, performance analysis, and debugging the agent workflow execution timeline.
Format: "YYYY-MM-DDTHH:mm:ss.sssZ" (e.g., "2024-01-15T14:30:45.123Z")
The first corrected version of the test code addressing compilation errors.
Contains the AI's initial attempt to fix the compilation issues while preserving the original business logic and test workflow. This draft represents the direct application of error correction strategies identified during the analysis phase.
The draft code demonstrates the AI's approach to resolving TypeScript compilation errors while maintaining the intended test functionality and following established conventions.
The test file that contained compilation errors with its detailed scenario metadata.
Contains the structured test file object that failed compilation before correction. The file includes its location, problematic source code content, and associated scenario information that provides context for understanding the compilation issues. This file serves as a comprehensive baseline for measuring the effectiveness of the correction process.
Unlike simple key-value pairs, this structure preserves the rich metadata about the test scenario, enabling better analysis of what specific test patterns or business logic implementations led to compilation failures and how they can be systematically improved.
OptionalfinalThe final production-ready corrected test code.
Contains the polished version of the corrected test code that incorporates all review feedback and validation results. This represents the completed error correction process, guaranteed to compile successfully while preserving all original test functionality.
The final implementation serves as the definitive solution that replaces the compilation-failed code and demonstrates the AI's ability to learn from errors and produce high-quality test code.
A unique identifier for the event.
The compilation failure details that triggered the correction process.
Contains the specific IAutoBeTypeScriptCompileResult.IFailure information describing the compilation errors that were detected in the test code. This includes error messages, file locations, type issues, or other compilation problems that prevented successful test code validation.
The failure information provides the diagnostic foundation for the AI's understanding of what went wrong and guides the correction strategy.
OptionalreviewAI's comprehensive review and validation of the corrected draft code.
Contains the AI's evaluation of the draft implementation, examining both technical correctness and business logic preservation. This review process identifies any remaining issues and validates that compilation errors have been properly resolved.
The review provides insight into the AI's quality assurance process and helps stakeholders understand how the correction maintains test integrity.
Iteration number of the requirements analysis this test correction was performed for.
Indicates which version of the requirements analysis this test correction reflects. This step number ensures that the correction efforts are aligned with the current requirements and helps track the quality improvement process as compilation issues are resolved through iterative feedback.
The step value enables proper synchronization between test correction activities and the underlying requirements, ensuring that test improvements remain relevant to the current project scope and validation objectives.
AI's deep compilation error analysis and correction strategy.
Contains the AI's comprehensive analysis of compilation errors and the strategic approach for resolving them. This analysis examines each error message to understand root causes, identifies error patterns, and develops targeted correction strategies while maintaining the original test purpose.
The AI correlates compilation diagnostics with business requirements to ensure that error corrections preserve the intended functionality. This deep analysis forms the foundation for all subsequent correction efforts, demonstrating the AI's ability to understand complex type errors and develop systematic solutions.
Detailed token usage metrics for the current operation.
Contains comprehensive token consumption data including total usage, input token breakdown with cache statistics, and output token categorization by generation type. This component-level tracking enables precise analysis of resource utilization for specific agent operations such as schema generation, test writing, or code implementation.
The token usage data helps identify optimization opportunities, monitor operational costs, and ensure efficient use of AI resources throughout the automated backend development process.
Unique identifier for the event type.
A literal string that discriminates between different event types in the AutoBE system. This field enables TypeScript's discriminated union feature, allowing type-safe event handling through switch statements or conditional checks.
Examples: "analyzeWrite", "prismaSchemas", "interfaceOperations", "testScenarios"
Event fired when the Test agent corrects compilation failures in the generated test code through the AI self-correction feedback process.
This event occurs when the embedded TypeScript compiler detects compilation errors in the test code and the Test agent receives detailed error feedback to correct the issues. The correction process demonstrates the sophisticated feedback loop that enables AI to learn from compilation errors and improve test code quality iteratively.
The correction mechanism ensures that test code not only compiles successfully but also properly validates API functionality while maintaining consistency with the established API contracts and business requirements.
Author
Samchon