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Agile+DevOps Culture in a Virtual World Transforming and maintaining culture is hard enough when team members are somewhat co-located and in physical spaces—even harder when the majority are working from home. |
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Balancing Testing and Delivery Times in Software Development So how can developers and their teams optimize testing procedures with the modern tools and tech available to them? Here, we will explore optimizing testing through experimentation to meet delivery deadlines and adapt to challenges in software development.
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Test, Test, Test Test, test, test. This is a phrase that has caught everyone’s attention this year as we grapple to mitigate COVID-19. The WHO states that testing is the only way out, as we cannot fight the pandemic blindfolded. |
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Evaluating Team Health in Agile and DevOps The importance of the human element in delivering great software is sometimes overlooked, as is the relationship between team health and team performance. Just like physical health checks, team checkups are important. Let's look at some factors that can affect team health and how you can evaluate the important metrics. |
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Making the Most of Your DevOps: A Slack Takeover with Gene Gotimer Thought leaders from the software community are taking over the TechWell Hub to answer questions and engage in conversations. Gene Gotimer, a senior architect at Coveros, hosted this Slack takeover and discussed all things DevOps, including whether you can have DevOps without agile and how you can reap all its benefits. |
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Comparing 4 Top Cross-Browser Testing Frameworks The market is flooded with cross-browser testing frameworks, with more options than ever before. How should you decide which option is best to test your application for compatibility with different web browsers? Let’s take a look at four of the top open source solutions today and compare their benefits and drawbacks. |
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5 Pitfalls to Avoid When Developing AI Tools Developing a tool that runs on artificial intelligence is mostly about training a machine with data. But you can’t just feed it information and expect AI to wave a magic wand and produce results. The type of data sets you use and how you use them to train the tool are important. Here are five pitfalls to be wary of. |
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Code Integration: When Moving Slowly Actually Has More Risk Many decisions about code branching models are made in the name of managing risk, and teams sometimes pick models that make integration harder in the name of safety. Moving slowly and placing barriers to change can seem safer, but agile teams work best when they acknowledge that there is also risk in deferring change. |
