(Part 1) RCA: The Evolution of Root Cause Analysis: From Traditional Software to Large Language Models
Root Cause Analysis (RCA) is crucial in software development for identifying and resolving core issues in applications. It follows a structured process:
- Reproduce the issue
- Gather information (error messages, logs, user reports)
- Analyze code and data flow
- Identify potential causes
- Test hypotheses
- Implement and verify fixes
To streamline this process, developers use tools such as:
- Debuggers (e.g., GDB, Visual Studio Debugger)
- Profilers for performance analysis
- Version control systems
- Automated testing suites
Software development’s heuristic nature allows for predictable issue tracking. Comprehensive unit and integration testing can prevent most incidents before production deployment. Effective RCA, combined with these tools and methods, leads to more robust and reliable software applications.
As AI becomes increasingly prevalent in our daily lives, Machine Learning (ML) techniques are significantly improving RCA in software applications. Here are key ML applications streamlining the RCA process:
- Automated log analysis
- Predictive maintenance
- Anomaly detection
- Error clustering
- Performance optimization
This list can go more and more with ML / DL evolution.
These ML techniques offer powerful enhancements to the RCA process, greatly improving efficiency and effectiveness. However, it’s important to recognize that they’re not infallible. Let’s explore how RCA is applied within ML systems themselves, as these advanced technologies can also experience failures.
RCA in ML is the process of identifying and understanding the fundamental reasons for model performance issues, errors, or unexpected behaviors. Its primary purpose is to diagnose and address the underlying causes of ML model shortcomings or failures.
Key Focus Areas for RCA in ML:
- Model performance issues
- Bias and fairness concerns
- Unexpected predictions or behaviors
- Data quality problems
These areas are analyzed using established mathematical techniques such as:
- SHAP (SHapley Additive exPlanations) values
- Feature importance analysis
- Partial dependence plots
The RCA process in ML is similar to traditional software development but with a crucial difference: ML models are inherently probabilistic, making it challenging to predetermine outcomes and maintain strict control.
The emergence of LLMs in various industries has introduced new challenges in RCA. As these models are deployed at scale in production environments, ensuring their reliability becomes crucial. While the fundamental purpose of RCA in LLMs remains the same as in traditional ML, there are significant differences due to their distinct architectures, capabilities, and challenges.
Comparison of RCA in Traditional ML vs. LLMs:
- Model Architecture: ML often uses structured data and specific algorithms (e.g., decision trees, SVMs), LLMs are based on transformer architecture, using vast amounts of text data.
- Input-Output Relationship: ML usually has clear input features and output targets, LLMs deal with complex, context-dependent natural language inputs and outputs.
- Interpretability: Many models (e.g., decision trees) in ML, offer some level of interpretability, LLMs generally are less interpretable due to their size and complexity.
- Error Types: ML often is focused on statistical errors, and misclassification, LLMs’ concerns include hallucinations, biases, and contextual misunderstandings.
- Performance Metrics: ML uses quantitative metrics like accuracy, precision, and recall, LLMs often require qualitative assessment of language output.
At Feedback Intelligence, our Insights feature a novel technique for automatic root cause analysis in LLM-based solutions. In our upcoming series, we’ll explore RCA in Retrieval-Augmented Generation (RAG) systems. We will discuss the layers of RCA done with RAG systems. We’ll address the challenges in identifying knowledge holes and fine-tuning parameters for improved retrieval performance.
co-author Haig Douzdjian
