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TM Integrating Automated Testing into DevOps
Jeffery Payne, Coveros, Inc.
Tue, 09/29/2015 - 1:00pm

Business demands and agile development processes are driving the pursuit of faster software releases, spawning a set of new practices called DevOps. At the forefront of the DevOps movement are rapid deployment, continuous integration, and continuous delivery—all of which require a high level of test automation across the application lifecycle. Join Jeffery Payne as he discusses the unique challenges associated with integrating automated testing into continuous integration/continuous delivery (CI/CD) environments. Learn how CI/CD works, appropriate tooling, and test integration points. Find out how to integrate your existing test automation frameworks into a fast release, DevOps environment. In addition to technology issues, Jeffery covers another key to DevOps success: the integration of development, testing, and operations processes—and people—with deep and honest communications among teams. Leave with a roadmap for integrating test automation with continuous integration and delivery so that every stakeholder has strong confidence that changes made to the code base will function as expected.

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Concurrent Sessions

W5 DevOps: Find Solutions, Not More Defects
Andreas Grabner, Dynatrace
Wed, 09/30/2015 - 11:30am

The promise of DevOps is that we can push new ideas out to market faster while avoiding delivering serious defects into production. Andreas Grabner explains that testers are no longer measured by the number of defect reports they enter, nor are developers measured by the lines of code they write. As a team, you are measured by how fast you can deploy high quality functionality to the end user. Achieving this goal requires testers to increase their skills. It’s all about finding solutions—not just problems. Testers must transition from reporting “app crashes” to providing details such as “memory leak caused by bad cache implementation.” Instead of reporting “it’s slow,” testers must discover “wrong hibernate configuration causes too much traffic from the database.” Using three real-life examples, Andreas illustrates what it takes for testing teams to become part of the DevOps transformation—bringing more value to the entire organization.

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W11 Test Strategies for Continuous Delivery
Melvin Laguren, Wrap Media
Wed, 09/30/2015 - 1:45pm

Classic testing methods and tools can’t keep pace with agile development practices and emerging continuous delivery models. Melvin Laguren describes how Macy's Ecommerce Merchant System uses a variety of tools to support its rapid delivery pipeline. A code coverage tool such as Karma determines if they need additional tests or if the existing tests provide satisfactory coverage. Using data analysis tools such as Code Maat with source code control, they have a robust approach to improve the code review process and easily identify necessary file changes to prevent issues in production. By incorporating these two tools, Melvin’s team members get real-time informative feedback that helps them make better testing decisions within their continuous delivery pipeline. Testers, developers, and all stakeholders have easy access to a dashboard reporting tool that showcases findings from unit, functional, and performance testing. Join Melvin to explore new tools and practices you can implement to smooth the road to continuous delivery in your systems.

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W17 Putting Quality First through Continuous Testing
Adam Auerbach, Capital One
Wed, 09/30/2015 - 3:00pm

Capital One has a highly integrated environment, which creates many dependencies for its agile teams. Because these dependencies are often not completed until late in their sprints, Capital One faced prolonged integration and regression testing phases, and did not realize the expected improvements in quality or time to market. As technology leaders pushed for continuous delivery, testing needed to “shift left” and execute test in real time concurrently with development. Adam Auerbach shares Capital One’s experience implementing continuous testing. He explains the core principles of continuous testing, service virtualization, and the continuous integration/continuous delivery pipeline—and why testers need to understand and leverage these important concepts. Adam believes that testers need to learn basic development skills, including Ruby and Java, so they can take advantage of advanced automation practices. Because continuous testing is not easy and many companies have large populations of manual testers, Adam will provide a learning map to help you plan your personal and team’s transition.

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T10 Deliver High-Quality Mobile Apps with Continuous Testing
Tom Chavez, SOASTA
Thu, 10/01/2015 - 11:15am

With hundreds of mobiles, tablets, and wearables running different versions of iOS and Android, automated continuous testing is the only sane way to address mobile testing. Tom Chavez presents a review of automated testing tools for mobile apps, including tools that require writing tests in a programming language  and tools that record mobile app actions for replay requiring no programming at all. To complement and complete your device test pool, Tom discusses when to use your own devices versus a remote device pool—even thousands of miles away from your development team. Learn how mobile app developers and test teams have made the transition from manual testing to automation. Leave convinced and ready with the resources and steps to integrate continuous automated testing into your mobile development process. Don’t let a fatal bug sneak into your apps, annoying users or, even worse, causing them to move on to your competitor.M

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T18 Proactive Performance Engineering in a DevOps Context
Sundar Narayanan, Wipro
Thu, 10/01/2015 - 1:30pm

DevOps methodologies require stronger cohesion between application development and operations teams. In the DevOps paradigm, performance engineering must mature beyond the usual measure-and-tune approach. Join Sundar Narayanan as he explores principles and techniques for proactive performance engineering. Sundar shares techniques for building predictive performance models from system designs parameterized with workload characteristics and expected resource demands.

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