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86 changes: 86 additions & 0 deletions .analysis/gson/java_integration/state.json
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"message": "Successfully created Gson instrumentation for dd-trace-java. The instrumentation instruments the com.google.gson.Gson class, tracking both serialization (toJson) and deserialization (fromJson) operations. \n\nCreated files:\n- GsonInstrumentation.java: Main instrumentation class using ForSingleType matcher for com.google.gson.Gson\n- GsonDecorator.java: Decorator extending BaseDecorator with span type 'json' and component 'gson'\n- GsonHelper.java: Helper class to manage CallDepthThreadLocalMap without direct class references\n- GsonTest.groovy: Instrumentation tests extending InstrumentationSpecification\n- build.gradle: Build configuration with muzzle directives for versions [1.6,)\n\nAll verification tasks passed:\nβœ“ Muzzle validation passed for Gson versions 1.6 through 2.13.2\nβœ“ Unit tests passed (3 tests: toJson, fromJson, nested calls)\nβœ“ Latest dependency tests passed\nβœ“ Code formatting (spotlessCheck) passed\n\nThe instrumentation correctly:\n- Creates spans with operation names 'gson.toJson' and 'gson.fromJson'\n- Sets span type to 'json' and component to 'gson'\n- Uses CallDepthThreadLocalMap to prevent recursive instrumentation\n- Follows dd-trace-java conventions for decorators, advice classes, and helper registration"
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{
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"additional_context": "Gson is a Java JSON serialization library. This is a non-HTTP instrumentation that tests decorator-only patterns (no client/server communication). Focus on instrumenting serialization/deserialization methods to track JSON processing operations. Use a lightweight decorator that captures the operation type and any relevant metadata. The span type should be 'json' and the component name should be 'gson'."
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"system_prompt": "You are a senior Datadog APM engineer working on the dd-trace libraries.\n\n## Your Role\n\nYou implement, fix, and maintain third-party library integrations for Datadog's distributed tracing and observability platform.\n\n## Priorities\n\n1. **Observability value** - Produce meaningful telemetry that helps customers understand their application behavior. Focus on traces, spans, and metadata that provide real insight into how applications perform.\n\n2. **Code quality** - Write clean, maintainable code that follows existing patterns in the codebase. Prefer simplicity over cleverness. Other engineers will review and maintain this code.\n\n3. **Production readiness** - Consider edge cases, error handling, and performance. Your code runs in customer production environments where reliability matters.\n\n4. **Test coverage** - Ensure integrations are properly tested with both unit tests and integration tests where appropriate.\n\n## Context\n\n- The ddtrace libraries instrument third-party packages to automatically capture distributed traces and send them to Datadog\n- Each integration wraps library methods to create spans, propagate context, and capture relevant metadata\n- Integrations should be non-intrusive and have minimal performance overhead\n- Follow the patterns established by existing integrations in the codebase\n",
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