Epic: track defect discovery and resolution time

[Guidevit]

Task: Identify and Capture the Timestamps for Defect Discovery and Resolution

Collaborate with the development and QA teams to understand the key stages in the defect lifecycle and establish where timestamps need to be captured. Typically, these stages include defect creation (discovery) and when the defect status is marked as resolved.
Develop integration with your bug tracking system (like Jira, GitHub Issues, or Bugzilla) to automate the capturing of these timestamps. This could be done via APIs or webhooks provided by these systems.
Make sure the timestamps are stored in a universal format to prevent any inconsistencies due to time zones or daylight saving time adjustments.
Task: Calculate the Time Duration Between Defect Discovery and Resolution

Once the timestamps are collected, develop a calculation process that subtracts the discovery timestamp from the resolution timestamp for each defect to measure the duration it takes to resolve an issue.
Make sure to handle edge cases, such as defects being reopened or multiple resolution timestamps.
Task: Implement Storage and Retrieval Mechanisms for Defect Discovery and Resolution Time Data

Design a database schema to store the defect information, including discovery and resolution timestamps, and calculated duration. The schema should be optimized for efficient storage and retrieval.
Depending on your tech stack, choose a suitable database technology (like MySQL, PostgreSQL, MongoDB). Consider factors such as scalability, reliability, and performance.
Implement robust database operations for inserting new data and retrieving existing data. Consider implementing caching mechanisms for frequently accessed data.
Task: Develop Visualizations for Defect Discovery and Resolution Time Metrics

Use a suitable data visualization library (like D3.js, Plotly, or Tableau) to create visual representations of the defect discovery and resolution time data.
Consider using different types of charts to represent the data, such as bar graphs for comparing discovery and resolution times across different defects, line graphs for tracking these metrics over time, or heatmaps to identify high-level trends.
Ensure that the dashboard includes functionality to filter and sort the data based on various parameters like date ranges, specific defects, or product modules. This will enable users to analyze the data from different perspectives.