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Is the Problem with Your Agile Tool, or How You’re Using It? While using index cards and a wall can function just fine as a kanban or Scrum board, issue-tracking tools such as Jira can make it easier to manage a backlog, especially with a distributed team. But these tools are more complex to use and can add their own overhead to the process. You need to keep things simple. |
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Selecting the Right Agile Framework There are many frameworks available to organizations that are maturing their agile process. However, some frameworks can help reinforce agile behaviors, while others can actually drive an organization to revert to waterfall habits. The right choice should be the methodology that allows teams to deliver their best work. |
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Quality Engineering in Agile and DevOps Ensuring that quality is advocated for at every step along the lifecycle can be tough. One easy response is, “Quality is everyone’s job”—after all, whole-team accountability is a key tenet of agile. But what does this really mean in practice? What approaches and roles help us embrace a culture of quality engineering? |
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Making (and Keeping) Project Risk Visible Project managers recommend how much should be invested to address various risks based on their understanding of project context, but the final decision about what to do and when those efforts are sufficient belongs to the sponsor. Risk management requires executive input, so sponsors need to see all risk data you have. |
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Fueling Innovation through Design Thinking Organizations must embrace new technologies in their product engineering efforts to stay ahead of the curve. But there is another quality that will be key this decade to giving product teams a proactive advantage: design. Design thinking should be embraced not just by designers, but by everyone involved with a product. |
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Good Process, Bad Process “Process” is a word that seems to have a lot of baggage. Depending on whom you ask, process is either essential to delivering value, or something that gets in the way. But this is the wrong way to frame the issue. A process is not inherently good or bad; it's how you use it, and whether it's right for your situation. |
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Machine Learning and Deep Learning: What's the Difference? Many people think that machine learning and deep learning are each just a fancy way to say artificial intelligence, but that is a misconception. Both terms represent subsets of AI technology, but they are different, and their differences dictate the functionality and application of these two software solutions. |
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Pros and Cons of Codeless Test Automation To create automated tests for software applications, testers have historically needed to be able to code in programming languages. Codeless testing eliminates the need for scripting from scratch every time, but in addition to its advantages, there are also some drawbacks. Is codeless automation right for your team? |
