Author: Neha Khandelwal, Software Design Assurance Engineering Manager, Zebra Technologies
Root Cause Analysis (RCA) is a critical yet time-consuming process in the realm of software quality assurance (QA). It requires meticulous investigation to pinpoint the underlying cause of defects, often involving sifting through logs, analysing code, and correlating various system behaviours.
QA teams spend significant effort identifying the root cause, which can delay resolutions and increase downtime. As systems grow more complex, traditional RCA methods become less efficient, demanding advanced tools to assist in automating and streamlining the process.
Enter generative AI (GenAI)—a burgeoning technology and useful tool for software quality assurance teams that holds the potential to transform RCA by rapidly identifying patterns, predicting causes, and even recommending solutions, ultimately accelerating bug detection and resolution.
One state of the industry survey found that the heaviest use of GenAI (50% of QA teams) is in the form of test data generation, followed by test case creation (48%). In terms of cognitive AI-based use cases, analysis of test logs and reporting is most prominent, especially in large organisations (38%), followed by AI for visual regression testing. The survey found that 30% of respondents believe that AI can enhance productivity in software quality assurance.
What is AI-powered RCA?
AI-powered RCA leverages AI and machine learning to automate the process of identifying the underlying causes of incidents or outages in software/IT systems. Traditional RCA methods require engineers to manually sift through logs, metrics, and telemetry data, which can be time consuming and prone to human error. AI-powered RCA, on the other hand, uses algorithms and models to analyse large datasets, detect patterns, and pinpoint the root cause faster and more accurately.
AI in RCA works by:
- Analysing historical data: AI models review past incidents and the corresponding telemetry data to identify common patterns and correlations.
- Automating correlation: Instead of manually correlating logs and alerts, AI can automatically detect relationships between events, leading to faster identification of the root cause.
- Anomaly detection: AI algorithms can identify deviations from normal behavior, flagging incidents even before they escalate.
- Learning and improving over time: As more data is processed, AI systems improve, becoming more efficient and accurate in predicting and diagnosing issues.
Incorporating AI into RCA not only reduces the time spent on troubleshooting, but also enhances accuracy, helping organisations resolve issues more quickly, minimise downtime, and avoid repeated incidents.
Here are some examples of how GenAI can be integrated with existing tools to enhance the RCA process, with example use cases:
- GenAI Integrated with SonarQube
Integration: Utilise a GenAI model trained on code quality, bug patterns, and best practices alongside SonarQube. This AI can analyse identified code issues and provide context-aware explanations and suggestions for fixes.
Example Use Case: Suppose SonarQube identifies a potential null pointer exception in the code. A GenAI model can analyse the issue, look at the context of the code, and suggest alternative code snippets or defensive coding strategies to prevent the exception. This suggestion is displayed directly in SonarQube’s dashboard for developers to review and implement.
- Auto-Generated API Issue Analysis with Postman
Integration: Integrate GenAI with Postman’s API testing environment to identify and diagnose API failures. The AI can simulate API calls, check the codebase, and provide detailed suggestions on what might have gone wrong.
Example Use Case: If an API endpoint fails during testing due to a timeout, the AI can analyse the request payload, server response, and recent code commits. It may pinpoint that a specific parameter format was modified and suggest reverting it. This analysis is then documented in Postman’s interface, and the suggested fix is directly provided to developers.
- Analysis using AI with Elastic Stack (ELK)
Integration: Integrate a GenAI model with the Elastic Stack to perform causal analysis of logs. The AI can read complex Kibana dashboards, detect anomalies, and generate natural language explanations about the underlying issues.
Example Use Case: If a specific microservice shows higher-than-normal latency, the AI integrated with Kibana could analyse logs, trace user interactions, and identify that the latency is due to an inefficient database query introduced in the latest update. It can then recommend query optimisations, explain the rationale, and simulate the impact of the fix.
- Code Context Analysis with GitHub Copilot X for RCA
Integration: Integrate GitHub Copilot X (or a similar GenAI code assistant) to analyse code errors within the development environment. The AI can access the codebase, review commit history and provide fixes directly in the IDE.
Example Use Case: Suppose an error is reported due to a recent code merge. The AI can check the changes in Git, simulate scenarios based on the new code, identify the commit introducing the bug, and offer a corrected code snippet. Additionally, it can generate an explanation of why the error occurred, aiding in understanding the root cause.
- GenAI for Detailed Root Cause in Dynatrace
Integration: Leveraging a GenAI model in Dynatrace can provide insights into detected anomalies and errors. The AI can perform deeper diagnostic evaluations based on performance metrics, logs, and traces.
Example Use Case: If Dynatrace identifies a memory leak, the GenAI can analyse the timeline of system events, correlate them with code changes, and provide a narrative on why the leak is happening, suggesting potential fixes like garbage collection optimisations. The AI can even suggest changes to specific lines of code that may be responsible for memory allocation issues.
By leveraging such integrations, we can turn RCA into a more proactive, data-driven, and intelligent process, reducing time spent on finding issues and increasing overall software quality.
Implications for Software Testers, QA Analysts and the Industry
The integration of AI-powered RCA is reshaping the responsibilities of software testers and QA analysts, highlighting the urgency of embracing this shift. By automating repetitive and time-intensive tasks such as sifting through logs and correlating system behaviours, AI allows QA professionals to focus on higher-value activities like improving processes and ensuring end-to-end quality.
However, this evolution also calls for testers to acquire new skills in AI-driven analysis and tool utilisation. With the increasing complexity of software systems, traditional RCA methods are rapidly becoming insufficient, making the adoption of AI tools a necessity for staying competitive and maintaining the speed and precision demanded in today’s software landscape.
The integration of AI-powered RCA in testing will also significantly alter the way QA teams address future challenges. One major shift will be the reliance on AI to predict and diagnose issues, which could reduce manual intervention but also create new complexities.
For instance, as AI systems analyse datasets to identify potential failures, testers will need to ensure the accuracy and fairness of these AI-generated insights. A challenge lies in validating the decisions made by AI, especially in scenarios where faulty predictions could lead to unnecessary changes or missed critical defects.
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