Instead of writing the same automation code repeatedly for the same actions, you can create keywords for those actions. When writing tests, you only need to call these keywords. This approach, known as creating a keyword-driven framework, makes test scripts more readable, maintainable, and efficient by avoiding redundancy.

In an era where data breaches generate headlines and data privacy is an increasing concern, how can organizations use the massive amounts of data they acquire without jeopardizing individual privacy? The solution could be found in a unique amalgamation of data anonymization approaches. As we dive into this fascinating topic, we discover how technology is transforming how we anonymize data, ensuring that the insights we gain are useful and respectful of privacy.

When you run your Go programs or integration tests, numbers of raw coverage files are typically generated and dumped into a directory specified by the GOCOVERDIR environment variable. These files contain valuable data about which parts of your code were executed during tests, offering a glimpse into your code’s effectiveness and robustness. However, sifting through these raw files to extract actionable insights can be daunting and unclear for many developers.

AI and generative Al can lead to major enterprise advancements and productivity gains. By offering new capabilities, they open up opportunities for enhancing customer engagement, content creation, virtual experts, process automation and optimization, and more.

When it comes to keeping your OAuth Client Credentials endpoints secure and high-performing, monitoring is crucial. Whether you’re a non-technical business owner, a software engineer, a student, a product owner, a web agency, or a DevOps professional, understanding and implementing effective monitoring can save you from a lot of headaches. In this article, we’ll dive into the top 10 best tools for monitoring OAuth endpoints, helping you find the best fit for your needs.

Like many other industries, fintech has also been witnessing the integration of cognitive technologies as a key driving force behind recent transformations. Powerful tools, such as artificial intelligence (AI) and machine learning, are empowering businesses with swift transaction processing, precise data analysis, and optimized resource utilization. Thus, it is apt to say that a future with sustainable fintech software development cannot be mutually exclusive to the aspirations of cognitive technologies.