CAST

1. New developers log in to CAST Imaging and select the application they need to understand.

2. CAST Imaging visualizes the application architecture, showing components, dependencies, and transaction flows.

3. Developers can drill down into individual code elements to understand their functionality and relationships.

4. CAST highlights potential issues and areas of technical debt.

5. Developers use CAST to identify the impact of changes before making them.

1. CAST Highlight analyzes the application codebase and identifies potential compatibility issues.

2. CAST provides recommendations for remediating these issues and preparing the application for cloud migration.

3. The team uses CAST to track progress and ensure a smooth migration process.

4. Post migration, CAST continues to monitor the app in the cloud, checking for performance regressions.

1. CAST Highlight identifies instances of technical debt, categorizing them by severity and impact.

2. Teams prioritize remediation efforts based on business impact.

3. Developers use CAST to understand the code and implement fixes.

4. CAST monitors the application over time to ensure that technical debt is reduced and does not re-accumulate.

1. Developers use CAST Imaging to visualize the application architecture and identify dependencies.

2. CAST predicts the potential impact of changes on other components and applications.

3. Developers can then make informed decisions about whether to proceed with the changes.

4. After the change, CAST re-analyzes the application.

1. CAST for AI analyzes the application codebase and creates a detailed map of its architecture.

2. This map is then used to provide AI tools with the context they need to understand the code.

3. AI tools can then be used to improve the application, for example by identifying and fixing bugs or by optimizing performance.

4. CAST provides ongoing monitoring of the app after AI changes.