Trace Data Analysis
Leverage comprehensive trace data analysis to enhance system integration testing and ensure product quality. Utilize a centralized toolchain for automated analysis, transforming raw data into actionable insights. And transition to a new modern format of data-driven testing process automation using the power of artificial intelligence (AI) tools.Simple text
- Visualization and Actionable Insights: Transform raw trace data into visual representations that highlight key insights and utilize AI tools for in-depth analysis. This combination of visualization and AI-driven automation helps identify and address errors early in the development process, significantly reducing the risk of issues in final production.
- Centralized Data Collection: Gather measurement logs from various cars and testing fields into a unified data pool. This centralized approach ensures that all trace data is accessible for analysis, streamlining the testing process.
- Scalable Analysis: Manage and scale the analysis process to accommodate varying volumes of trace data. The toolchain and centralized storage are designed to handle large datasets and complex analysis requirements.
- Cross-Department Collaboration: Facilitate collaboration between departments by providing a shared platform for trace data analysis. Different teams can contribute their expertise, leading to more comprehensive and thorough testing.
- Integration with Azure: Upload trace data to Azure for enhanced integration testing. The data is linked with Metadata such as Sprint, Configuration, or car number, enabling detailed and organized analysis.
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