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EXTENDING.md

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 # Creating new RCA datasets and Developing new RCA methods
 
-TBW
+## Creating new RCA datasets
+To create new RCA datasets, follow these steps:
+
+1. **System Setup**: Deploy the target microservice system in a controlled environment, such as a Kubernetes cluster, and configure it to generate telemetry data (metrics, logs, traces).
+
+2. **Fault Injection**: Identify the fault types to include (e.g., resource, network, code-level faults). Use tools like `stress-ng` for resource faults, `tc` for network faults, and manual code modifications for code-level faults.
+
+3. **Data Collection**: 
+   - **Metrics**: Use tools like Prometheus and cAdvisor to gather system metrics.
+   - **Logs**: Employ log aggregators like Fluent Bit or Loki to collect and structure logs.
+   - **Traces**: Use tracing tools like Jaeger to capture distributed traces.
+
+4. **Fault Annotation**: Annotate the collected data with labels for the injected faults, including:
+   - The time of fault injection.
+   - The root cause service.
+   - Specific root cause indicators (e.g., a metric, log entry, or trace span).
+
+5. **Data Processing**: Format the telemetry data into a structured format like CSV or JSON. Ensure consistency by including columns for timestamps, service names, and telemetry data values.
+
+6. **Validation**: Engage domain experts to validate the dataset for accuracy and completeness.
+
+7. **Documentation**: Provide a README or similar file with details about the dataset, including:
+   - The systems used.
+   - Fault types included.
+   - Instructions for downloading and using the dataset.
+
+## Developing new RCA methods
+To develop new RCA methods and integrate them into RCAEval, follow these steps:
+
+1. **Define the Approach**:
+   - Decide on the type of RCA method (metric-based, trace-based, multi-source).
+   - Determine the algorithm or technique to use (e.g., statistical analysis, causal inference, machine learning).
+
+2. **Implement the Method**:
+   - Create a new Python file in the `RCAEval/e2e/` directory, naming it appropriately (e.g., `new_method.py`).
+   - Implement the method as a Python function with the following signature:
+     ```python
+     def new_method(data, inject_time=None, dataset=None, sli=None, anomalies=None, **kwargs):
+         # Method logic here
+         return {
+             "ranks": ranked_root_causes,
+         }
+     ```
+
+3. **Preprocess the Data**:
+   - Use existing utilities from `RCAEval.io.time_series` to preprocess the input telemetry data, such as `preprocess`, `drop_constant`, or `select_useful_cols`.
+
+4. **Analyze the Data**:
+   - Implement the core logic for root cause analysis.
+   - Rank the root cause candidates based on their likelihood of causing the failure.
+
+5. **Test the Method**:
+   - Write unit tests in `tests/test_new_method.py` to ensure correctness and reproducibility.
+   - Use sample datasets available in RCAEval to validate the method.
+
+6. **Integrate with RCAEval**:
+   - Add the method to `RCAEval/e2e/__init__.py` for seamless import.
+   - Update the `main.py` evaluation script to include the new method by adding it to the `--method` options.
+
+7. **Document the Method**:
+   - Provide usage examples in the README or a dedicated tutorial notebook in the `docs/` folder.
+   - Include a description of the method, its assumptions, and limitations.
+
+8. **Contribute Back**:
+   - Submit a pull request to the RCAEval repository with the new method and associated documentation.