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Reduce risk and accelerate digital transformation with IBM ADDI

1 Posted by - June 5, 2017 - Blog

By Mark Indermaur, Offering Manager, Application Discovery & Delivery Intelligence

As our mainframe customers push to transform applications to better serve customers, they need to minimize the risk of changes to these business-critical systems while improving productivity.  Application discovery and analytics tools can address these challenges by combining data from analysis of your application environment with data that is produced throughout the DevOps lifecycle like code, designs, test cases, test execution records and operational information about applications.

IBM Application Discovery and Delivery Intelligence (ADDI) is the most comprehensive such tool on the z/OS market and the only tool that:

  • Analyzes a broad range of IBM and non-IBM programing languages, databases, workload schedulers, and environments. Enterprise application portfolios were built over decades using an ever-evolving set of technologies, so you need a tool with broad support, such as ADDI, to truly understand the relationships between application components and to accurately determine the impacts of potential changes.
  • Integrates with mainframe environments and tools via a z/OS agent to automatically synchronize application changes. Without keeping your application analysis synchronized with the latest changes that your developers made, your analysis can get out of date and you risk missing critical changes.
  • Provides powerful visual analysis integrated with leading IDEs. When modifying complex applications, you need to be able to quickly navigate the dependencies between application components and drill down to see relevant details. After you understand the code, you need to be able to modify it. The integration between ADDI and IBM Developer for z (IDz) combines the leading IDE with the application understanding and analytics capabilities you need to safely and efficiently modify the code.
  • Cognitively optimizes your test suites.  When you have a large code base to maintain and a lot of tests to run, you must ensure that you spend resources efficiently to run tests more optimally. ADDI correlates code coverage data and code changes with test execution records to enable you to identify which regression tests are the most critical to run, allowing you to optimize time and resources while reducing risk. It exposes poorly tested or complex code and empowers the test teams with cognitive insights to turn awareness of trends into mitigation of future risks.
  • Intelligently identifies performance degradations before they hit production. ADDI correlates runtime performance data with application discovery data and test data to quickly pinpoint performance degradation and narrow down the code artifacts that are relevant to the cause of bad performance. This enables early detection of performance issues and speeds resolution.
  • Supports multiple national languages. Your global development team members including those from out-sourcing suppliers are more productive when they can use development tools in their native language.

Only IBM Application Discovery and Delivery Intelligence can analyze a broad range of application environments and data. ADDI is integrated with the tools that your teams need to accelerate your digital transformation while reducing the risk of application changes. Do not settle for narrow analysis that does not leverage the application lifecycle data you already have. Read the Forrester Total Economic Impact of ADDI to see how clients deliver technology services faster and better with IBM ADDI.

About the author:

Mark Indermaur, Offering Manager, Application Discovery & Delivery Intelligence

Based in Research Triangle Park, NC, Mark leads the offering lifecycle for Application Discovery & Delivery Intelligence and related products including strategy, requirements, plans, marketing, and support. He has led cross-functional teams responsible for product management, technical sales, business development, and services in companies ranging from startups to IBM. Mark has an MS in Computer Science from North Carolina State University and a BS in Electrical Engineering and Mathematics from Duke University.

 

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