Cognitive DevOps: Using Data to Improve DevOps Practices

Technology and analytics are being used to improve processes in all areas of business. Software development is no exception, and the leading driver of this revolution is DevOps. At SHARE San Jose 2017, IBM developer Gary Mazo spoke about cognitive DevOps: leveraging data to help teams work more effectively through analysis, insight, and optimization.

Analyzing data helps rapidly identify the areas of concern in your application portfolio, like low-performing areas of code or redundant test cases, then allows you to assess the impact, scope, risk and actions required to fix them. Through analysis, you can improve planning accuracy and efficiency by optimizing the actions needed to build, test and deploy applications.

There are a number of tools available that can help you gain insights to optimize work processes. In his SHARE Live! Presentation, Mazo outlined three of IBM’s most commonly used analytics products, as well as how these tools can help DevOps teams.

IBM’s Main Offerings

IBM offers a couple of relevant services, each with a slightly different tailored offering, according to Mazo. OMEGAMON for CICS Integration, for example, identifies potential performance bottlenecks early, measures transaction execution and failure frequency and performs a trend analysis of performance and resource consumption metrics.

Application Discovery and Delivery Intelligence (ADDI) is a powerful set of static analysis tools, capable of analyzing up to 99 percent of the code on the mainframe. ADDI provides insight into application maintainability, application inventory and complexity, and trend analysis of the software quality and complexity metrics.

The third product is a code coverage provider, which identifies how much of an application has or has not been executed. Code coverage collects run time execution information, aggregates it, and reports how much of an application has been tested, with the end result being reliable, consistently performing business applications.

How Data Analysis Helps DevOps Teams

Mazo explained that the insights and analysis gleaned from newly discovered data are incredibly useful to DevOps teams.

By getting access to previously hidden operations data through OMEGAMON, he said, developers and test teams can easily detect performance and resource issues throughout the development lifecycle. By combining operations data and development code in ADDI, a development team can immediately identify business critical programs and get a comprehensive view of the application health, so you can figure out what parts of the application code affected operations.

Using one of these tools makes it much easier to view and access available but hard-to-obtain data. In addition, you have the opportunity to bring together different sets of data for deeper insights, which ultimately benefits the whole DevOps team. Cognitive DevOps is one way to leverage data analysis to improve organizations’ DevOps practices.

Watch Gary Mazo’s full presentation, “Cognitive DevOps: Get Rid of the Guesswork to Improve Software Delivery,” as well as other presentations about DevOps, in the SHARE Content Center.

 

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