Beyond Code Coverage

Beyond Code Coverage: Understanding the Ripple Effect of Change

March 18, 2013

Code coverage tools provide some basic metrics about the percent of your code that’s covered by an automated test. But it lacks the ability to understand the ripple effect of change or help you prioritize your automated testing efforts. In this session, you will learn how Coverity was able to transform its testing process through insourcing, automation and by focusing on the most critical code and code impacted by change. We’ll discuss how we incorporated development testing into our agile process and implemented stage gates at various points in our SDLC based on specific quality and security criteria and metrics. Today, 97% of our end-to-end tests are now automated, we can complete a full round of end-to-end tests in two days and with three engineers; and our maintenance release cycle has been reduced from two weeks to three days.

About the Speakers

Al Bessey

Al Bessey has been a Customer-Facing Software Solutions Engineer at Coverity since December 2007. He has his Master’s degree in Applied Mathematics from the University of South Carolina, specializing in parallel algorithms for parallel multi-processor systems. With over 20 years of field sales and support experience for hardware systems, software applications and services, Al is an expert in development testing.

Ed Tice

Ed Tice has been a Customer-Facing Software Solutions Engineer at Coverity since June 2008. He studied Computer Science at Drexel University specializing in testing and optimization of polymorphic programs. With over 15 years of experience developing enterprise systems, Ed is an expert in development testing.

Posted in Meetings
AJUG Meetup

Data Microservices with Spring Cloud Stream, Task, and Data Flow

Tuesday, July 19, 2016

Microservice based architectures are not just for distributed web applications! They are also a powerful approach for creating distributed stream and batch processing.

Spring Cloud Data Flow enables you to create and orchestrate standalone executable applications that communicate over messaging middleware such as Kafka and RabbitMQ that when run together, form a distributed stream processing application. It also allows users to create and orchestrate short lived microservices like batch jobs or boot applications that perform a task and then terminate when complete.

This allows you to scale, version and operationalize stream processing and task applications following microservice based patterns and practices on a variety of runtime platforms such as Cloud Foundry, Apache YARN and others.


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