QA Increasingly Benefits from AI and Machine Learning

By Erik Fogg

The needle in quality assurance (QA) testing is moving in the direction of increased use of artificial intelligence (AI) and machine learning (ML). However, the integration of AI/ML in the testing process is not across the board. The adoption of advanced technologies still tends to be skewed towards large companies. Some companies have held back, waiting to see if AI met the initial hype as being a disruptor in various industries. However, the growing consensus is that the use of AI benefits the organizations that have implemented it and improves efficiencies.

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Key questions to ask before hiring a QA vendor

By Adam Rush

Developers and publishers spend a great deal of time planning all aspects of their game's development. Often these companies reach out to third-party industry vendors to help lighten the load and stress. One of the more common pillars that is partially outsourced is Quality Assurance (QA). That's because whether developers build QA into their plans from the beginning or not, unforeseen fires can start in production that need putting out.

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3 Reasons to Pentest with Brave

By Ochaun Marshall

Penetration testing is a race against the clock. Often, we only have a few days to examine all the functionality of a web application or an API. That is why we spend a lot of time refining and modifying our pentesting workflow to shave off any inefficiencies. This process often requires a re-evaluation of the tools we bring to the task. I’m going to give you 3 reasons why you should switch to Brave as your pentesting browser.

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How AI improves microservices testing automation

By Joydip Kanjilal

Organizations that adopt artificial intelligence (AI) in testing of microservices-based applications gain better accuracy, faster results, and greater operational efficiency. AI and machine-learning technologies have matured over the last few years, and today their application in automated testing can help in more ways than one. In fact, AI has redefined the way microservices-based applications are tested, especially when it comes to canary testing.

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Why We Need ML Ops: 4 Things to Consider When Testing AI

By Stephan Jou

MLOps – a compound of “machine learning” and “operations” – is a newly emerging best practice in the enterprise space that is helping data science leaders effectively develop, deploy and monitor data models. According to new research, the MLOps market is only predicted to grow in the coming years, and is predicted to reach almost $4B by 2025. With such rapid growth, it’s important that businesses prioritize MLOps innovation now.

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New tool developed to test reliability of AI algorithms

By Mingxi Cheng

Now, a new tool developed by USC Viterbi Engineering researchers generates automatic indicators if data and predictions generated by AI algorithms are trustworthy. Their research paper, "There Is Hope After All: Quantifying Opinion and Trustworthiness in Neural Networks" by Mingxi Cheng, Shahin Nazarian and Paul Bogdan of the USC Cyber Physical Systems Group, was featured in Frontiers in Artificial Intelligence.

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