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 test function that contained compilation errors.
Contains the specific test function object that failed compilation, including its metadata, location, and source code. This can be any type of test function: prepare, generation, authorization, or main test write function. The function type determines which specialized correction strategy will be applied.
A unique identifier for the event.
Function calling trial statistics for the operation.
Records the complete trial history of function calling attempts, tracking total executions, successful completions, consent requests, validation failures, and invalid JSON responses. These metrics reveal the reliability and quality of AI agent autonomous operation with tool usage.
Trial statistics are critical for identifying operations where agents struggle with tool interfaces, generate invalid outputs, or require multiple correction attempts through self-healing spiral loops. High failure rates indicate opportunities for system prompt optimization or tool interface improvements.
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.
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.
Detailed token usage metrics for the operation.
Contains comprehensive token consumption data including total usage, input token breakdown with cache hit rates, and output token categorization by generation type (reasoning, predictions). This component-level tracking enables precise cost analysis and identification of operations that benefit most from prompt caching or require optimization.
Token usage directly translates to operational costs, making this metric essential for understanding the financial implications of different operation types and guiding resource allocation decisions.
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", "databaseSchema", "interfaceOperation", "testScenario"
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