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Prioritizing Invisible Work There are work items that will give the team an operational boost and perhaps avoid a crisis, but that never make it to the top of the priority list—like build and deployment improvements, or paying down technical debt. For enabling work that is valuable but too invisible to be a priority, consider breaking it down. |
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Software Features to Avoid in a Production Environment When developing an application, it’s best practice not to use certain software features in a production environment. These include features related to programming language, the OS, the database, a framework, a web or application server, or a tool. You have to consider the production setup to avoid bugs or server crashes. |
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Team Agility in a Post-Pandemic World COVID-19 has necessitated entirely remote environments, and people the world over have had to inspect their foundations of working, adapt to a new way of remote execution, and integrate their personal and professional lives more than before. Organizational leaders need to embrace a new outlook in four critical areas. |
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What’s the Problem with User Stories? Agile projects focus on very lightweight, simple requirements embodied in user stories. However, there are some problems with relying solely on user stories. They often don't contain enough accuracy for development, testing, or industry regulations. There's a better way to write detailed requirements that are still agile. |
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Testing in the Dark Requirements only go so far in identifying areas to test. Sometimes testers are given no information at all, leaving it up to them to determine what to test. Don’t accept the need to indiscriminately test with no clear understanding. Your testing should be targeted, and these techniques will help focus your test effort. |
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Leave No Tester Behind Creating comprehensive automated tests within a sprint can be a challenge. If the testers don't finish the automation and the rest of the team moves on, testers get left behind and can't catch up. You need some techniques to keep everyone together and ensure that all essential work is accomplished—including test automation. |
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Lessons the Software Community Must Take from the Pandemic Due to COVID-19, organizations of all types have had to implement continuity plans within an unreasonably short amount of time. These live experiments in agility have shaken up our industry, but it's also taught us a lot of invaluable lessons about digital transformation, cybersecurity, performance engineering, and more. |
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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. |
