Technical Overview
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SeaLights operates as a code coverage and testing optimization platform, offering valuable insights into your software quality throughout the development lifecycle. Let's delve into the core components of its architecture:
Data Collection:
Build Scanner: Analyzes code changes and builds an intelligent map of your codebase, identifying modifications made in the latest build. This agent acts as the initial point of contact, scanning all binaries and artifacts.
Test Listener: Collaborates with your existing test management tools, tracking test execution and pinpointing which tests cover specific code areas. This agent essentially listens to and analyzes the testing process.
Data Processing and Analysis:
Data Analysis Engine: Combines the data collected by both agents, calculating various test coverage metrics and identifying any gaps in your testing strategy. It acts as the central processing unit, bringing together the information gathered by the agents.
Actionable Insights:
Reporting and Visualization Engine: Transforms the raw data into comprehensive reports and clear visualizations, providing actionable insights to guide your decision-making. This component presents the data in a user-friendly format, helping you understand the collected information and make informed decisions.
SeaLights in Action:
Code Changes Detected: The Build Scanner identifies modifications in your codebase.
Test Execution Tracked: The Test Listener monitors your existing test suites and tracks their coverage.
Data Analysis and Insights: The Data Analysis Engine combines this information, calculating coverage metrics and identifying untested areas.
Actionable Reports: The Reporting and Visualization Engine generates reports and visualizations, highlighting potential risks and areas for improvement.
Through this well-defined architecture, SeaLights empowers you to:
Gain comprehensive visibility into your software's code coverage across various testing types.
Identify untested code and prioritize areas needing additional testing efforts.
Optimize your testing process by focusing on the most relevant tests based on code changes.
Make data-driven decisions to ensure the quality and stability of your software releases.
