Agile + DevOps USA 2024 - Developer

The DevOps movement is here. Companies across many industries are breaking down siloed IT departments and federating them into product development teams. Testing and its practices are at the heart of these changes. Traditionally, IT organizations have been staffed with mostly manual testers and a limited number of automation and performance engineers. To keep pace with development in the new “you build it, you own it” environment, testing teams and individuals must develop new technical skills and even embrace coding to stay relevant and add greater value to the business. DevOps really...

Executive leaders across the globe have been asking a relatively simple yet profound question: Can we leverage generative AI to transform our business, enterprise, or industry? For software-based companies, focus has either been on differentiating their product and service offerings using this new technology. But how about leveraging generative AI to improve team productivity and efficiency? This may be possible but how do you measure its success? What are some of the key use cases within business analysis, development, and testing that software teams can use? Are there any pitfalls...

Although most agile developers would prefer to use Test-Driven Development (TDD) on everything, their products or systems usually contain existing untested "legacy" code which resists and stifles good TDD practices. Untested code is easily broken and can hide those defects for months. Even with TDD, a developer adding new behaviors to legacy code can quickly run into problems when that untested code must change. Characterization Testing (i.e., testing existing behaviors) is the ancillary technique that allows teams to strengthen their safety net of fast, automated tests. But adding tests...

Much attention is given to machine learning model training and testing in the industry. While these activities are essential for producing a production-ready machine learning model, organizations face some critical business challenges that must be addressed when building and testing machine learning models. Things like the reproducibility of results, accuracy of predictions, reusability of components, and trackability of experimentation are all vital to the success of any application.  The term MLOps has emerged as a method for applying DevOps practices and automation to the machine...

Envision a DevOps realm infused with AI-driven superpowers, where a reliable AI companion revolutionizes the way we approach development. This companion doesn't just assist; it transforms the adventure of creating software that spanning the full spectrum of SDLC possibilities and pinpointing bugs in the most unexpected places. This is the essence of AI-augmented with human-in-the-loop, a paradigm where AI elevates engineers to maestros, orchestrating a symphony of enhanced augmented subject matter intelligence. By turbocharging the testing process to be quicker, smarter, and more efficient...

Our industry has been transformed by new technologies, radically changing how we work. Thirty years ago, deployment cycles took 3-5 years; today, they happen in seconds. This rapid pace is largely due to our focus on investing in tools that boost developer productivity. We're still driven to create tools that simplify developers' work and make it easier for others to join the field. Many companies are heavily invested in enhancing productivity. However, the abundance of productivity tools doesn't automatically lead to increased productivity. Companies need to examine and adapt their...

Cloud-native architecture involves breaking down software components into smaller, loosely coupled applications known as microservices. While this approach can effectively address scalability issues in the cloud, it can also introduce greater complexity. Identifying the root cause of a microservice failure can be challenging if there is not enough data available. As such, a well-designed cloud-native system should incorporate logs, metrics, and traces to facilitate effective root cause analysis. OpenTelemetry offers simple and effective tools for implementing observability in a...

In today's rapidly evolving tech landscape, transitioning to agile and DevOps practices is crucial for organizational growth. However, many teams struggle to gauge their progress effectively within these methodologies. In this insightful session, Cholette will explore the key metrics that drive success in agile and DevOps adoption, focusing on best practices that can be applied across various organizational settings. Delving into essential metrics drawn from industry best practices, Cholette will shed light on the critical indicators for understanding progress and fostering...

There is a lot of hype about Generative AI automatically creating software applications. Some see this as the death of the software engineering profession, while others believe AI technology will simply help increase the productivity of programmers. The reality is probably somewhere in the middle. This session explores this question and uses demonstrations of commercial and open-source AI assistants to show what is and is not possible with today's GenAI technology. A high-level discussion of how these models work will be included to give the audience some context for what they can and...