InfoQ Homepage IT Service Management Content on InfoQ
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Data-Driven Decision Making – Product Development with Continuous Delivery Indicators
The Data-Driven Decision Making Series provides an overview of how the three main activities in the software delivery - Product Management, Development and Operations - can be supported by data-driven decision making. In Development, Continuous Delivery Indicators can be used to steer the efficiency of the development process.
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Three Key Success Factors for Improving Test Automation Outcomes
Test automation is crucial in the DevOps world and vitally important even if not taking a DevOps approach, and good test automation requires careful thought and design from the architecture onward. Tests need to be fully automated, and that automation needs to be stable; no test cases should fail for reasons other than issues in the system(s) under test.
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Q&A on the Book Team Topologies
The book Team Topologies by Matthew Skelton and Manuel Pais shows how to arrange teams within an organization to enable effective software delivery. It describes four fundamental team types and three team interaction patterns, and dives into the responsibility boundaries of teams and how teams can communicate or interact with other teams.
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Testing Microservices: Six Case Studies with a Combination of Testing Techniques - Part 3
This article presents six real world use cases of testing microservice-based applications, and demonstrates how a combination of testing techniques can be evaluated, chosen, and implemented.
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Velocity and Better Metrics: Q&A with Doc Norton
Velocity is not good for predictions or diagnostics, argued Doc Norton at Experience Agile 2019. It's a lagging indicator of a complex system which is too volatile to know what our future performance will be; it isn’t stable enough to be used reliably. We can use Monte Carlo simulation for forecasting, and cumulative flow diagrams to track work, see changes in scope, and spot bottlenecks.
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Q&A on the Book Continuous Delivery in Java
The book Continuous Delivery in Java by Daniel Bryant and Abraham Marin-Perez was released nearly ten years after the original Continuous Delivery book by Dave Farley and Jez Humble, and more than 20 years after Java’s first release. Q&A with the authors to better understand from their experience why a book on Continuous Delivery specifically for Java and the JVM ecosystem was needed.
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Unlocking Continuous Testing: The Four Best Practices Necessary for Success
While the majority of organizations have enthusiastically embraced agile planning and development, most still find themselves unable to effectively implement continuous testing throughout the software development lifecycle. There are four best practices to help overcome this: focus on test quality, keep your tests short and atomic, test across multiple platforms, and leverage parallelization.
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The Pipeline Driven Organization - Enabling True Continuous Delivery
Many organizations try to implement continuous integration or continuous delivery, but they get stuck in the process; too many human bottlenecks standing between the pipelines. By teaching pipelines to make better decisions and offloading human judgements onto the pipelines we can have the pipelines make decisions all the way up to production to create a true continuous delivery mechanism.
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Deploying Docker Containers Using an AWS CodePipeline for DevOps
In this walkthrough, learn how to perform continuous integration and deployment of Docker containers with no downtime using AWS CodePipeline and Amazon Elastic Container Service (ECS).
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DevOps and Cloud InfoQ Trends Report - February 2019
An overview of how the “cloud computing” and DevOps space is evolving in 2019 including updates on Kubernetes, Chaos Engineering, Service meshes and more.
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Continuous Delivery - It’s Not All about Tech!
It’s easy to get caught up in the technical side of continuous delivery. Objectively observing all stages of the releases in action allows measuring the release process to find non-tech factors hindering your releases and the bottlenecks and queues. Make sure your communication methods are effective, and that all the people involved are genuinely working together well.
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What Machine Learning Can Learn from DevOps
The fact that machine learning development focuses on hyperparameter tuning and data pipelines does not mean that we need to reinvent the wheel or look for a completely new way. According to Thiago de Faria, DevOps lays a strong foundation: culture change to support experimentation, continuous evaluation, sharing, abstraction layers, observability, and working in products and services.