Justin Reock

Over the last decade, DevOps has emerged as an influential business philosophy and practice, helping businesses drive high quality software to market faster. DevOps focuses on the elimination of bottlenecks that occur when development and operational resources are too divorced from one another. But what about friction in the development and test process? What about the delayed feedback cycles that come from slow builds and test flakiness? How can we reduce friction in areas that are outside of the focus of DevOps? The presentation will include examples of DPE practices in action from Java projects using the Maven or Gradle build tool.

Attendees will walk away from this presentation with a better understanding of:

• Acceleration technologies for speeding up feedback cycles
• How to use data analytics to quickly determine the root cause of problems and prevent problems from happening in the first place
• The costs of a low productivity environment with wasted time waiting for builds, tests, and CI/CD pipelines
• The importance catching errors earlier, including incorrect signals like flaky tests
• How to make the practice of developer productivity engineering a respected discipline

A barrage of software philosophies have hit the industry over the last few years, all claiming to reduce friction in digital transformation. But what does that mean organizationally and where does open source play into all of this?

We'll talk about the roots of DevOps in the Theory of Constraints, how 12-Factor principles can guide your microservice and cloud migration refactor efforts, and how building an optimal strategy for the use of open source within your organization can tie all of these concepts together.

What if we could achieve completely ‘contactless’ software security scanning? As the lines between physical and digital security become blurrier and blurrier, software quality standards and testing methodologies must continue to keep pace. Software fuzzing has long been a trusted method for finding vulnerabilities that are difficult to discover using traditional methods.

The application of AI and ML to this field has already begun to bear very promising results. By leveraging deep learning techniques to improve our input corpus and better understand our program's states, we can shine areas on the code logic that would be hidden by approaches like vulnerability scanning and static code analysis, and even traditional software fuzzing.