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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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The Real Value of Cross-Functional Agile Teams Agile teams know that cross-functional collaboration is central to the methodology, but there are often barriers to fully embracing this idea. If teams are used to handoffs, it may seem like it makes sense to maintain the status quo. Try collaborating on something small to realize the true value of cross-functional teams. |
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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. |
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Choosing the Right Threat Modeling Methodology Threat modeling has transitioned from a theoretical concept into an IT security best practice. Choosing the right methodology is a combination of finding what works for your SDLC maturity and ensuring it results in the desired outputs. Let’s look at four different methodologies and assess their strengths and weaknesses. |
