BMC AMI boosts developer confidence for refactoring mainframe code

With the BMC AMI DevX Code Insights solution, next-generation developers can use automated intelligence to understand and break apart monolithic mainframe programs for faster changes to complex code. The BMC AMI zAdviser Enterprise solution drives continuous improvement using AI/ML to accelerate the pace of mainframe DevOps transformation.

"Developers often fear making changes to monolithic mainframe applications, because they lack the understanding of how these large, complex programs work. Refactoring the code into smaller, callable modules provides greater responsiveness to make the changes businesses demand," said Katie Norton, senior research analyst, DevOps and DevSecOps at IDC. "With the ability to automatically and intelligently visualise and refactor code, developers can focus on releasing high-quality mainframe applications faster to innovate and accelerate their mainframe transformations."

Delivering Insights That Reduce Complexity for Greater DevOps Effectiveness

With the introduction of the BMC AMI DevX Code Insights solution, developers can maintain and extend their applications with confidence to:

• Understand how the application works in real time with runtime visualisation

• Break apart monolithic programs into smaller, more manageable subprograms to isolate business logic and enable APIs

• Quickly remove dead code

• Analyse data flow and debug code

“BMC is empowering the next generation of developers and accelerating innovation for organisations,” said John McKenny, senior vice president and general manager of Intelligent Z Optimization and Transformation at BMC. “BMC AMI DevX Code Insights and BMC AMI zAdviser Enterprise make the mainframe as approachable as any other platform, welcoming in a new era of fast and fearless mainframe application development.”

Accelerating Mainframe DevOps Transformation and Continuous Improvement

The BMC AMI zAdviser Enterprise solution helps DevOps managers harness the full potential of telemetry data using AI/ML to:

• Gain valuable insights into the adoption of DevOps tools

• Continuously improve debugging and testing

• Monitor critical metrics, including lead time for change, change failure rate, deployment frequency, and mean time to restore service

• Uphold code quality by detecting abnormal levels of regressions in application code