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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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Benefits of Using Columnar Storage in Relational Database Management Systems Relational database management systems (RDBMS) store data in rows and columns. Most relational databases store data row-wise by default, but a few RDBMS provide the option to store data column-wise, which is a useful feature. Let’s look at the benefits of being able to use columnar storage for data and when you'd want to. |
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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. |
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3 Software Testing Lessons from an Unlikely Source With people trying to stay isolated as much as possible due to COVID-19, going to the grocery store suddenly became something to strategize. At least making a plan, prioritizing risk, and being agile are business as usual for software testers. Here are some of the top lessons testers can learn from our current situation. |
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Comparing Apache Sqoop, Flume, and Kafka Apache Sqoop, Flume, and Kafka are tools used in data science. All three are open source, distributed platforms designed to move data and operate on unstructured data. Each also supports big data in the scale of petabytes and exabytes, and all are written in Java. But there are some differences between these platforms. |
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Defensive Design Strategies to Prevent Flaky Tests Flaky tests could be the result of issues in the code, but more often they are due to assumptions in the test code that lead to non-relatable results. There are many reasons that tests can fail intermittently, and some can be easily avoided by applying good defensive design strategies. It's all about making your code agile. |
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Shifting Security Left in Your Continuous Testing Pipeline Security is often the black sheep of testing—an afterthought that gets only a scan before release. We have to make security a first-class testing citizen with full-lifecycle support. For the best impact, introduce security testing into the early phases of the continuous testing pipeline. Here are some tools to help. |
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Strategically Using Slack Time after a Release When you've worked for months on a big software release, afterward you may want to jump into the next project. But building in some slack time between sprints is a good idea. After a big release, there will probably be more work as new users discover bugs in your software. Plan for some more testing and development. |
