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In the 21st century, data has become an invaluable asset that drives innovation, decision-making, and progress across various domains. Its importance stems from the fact that we are living in a digital age where almost every aspect of our lives generates, consumes, and relies on data. When it comes to software testing, data plays a crucial role to replicate positive/negative cases and identify corner or edge cases that might remain untested without conducting testing on various data values.

Robotic process automation (RPA) is a software system automation approach that employs pre-programmed “bots” to automate manual tasks, typically through a user interface (UI). RPA tools are primarily used to automate business processes that once depended on human resources and were highly repetitive and operational in nature. Many of these business processes are rooted in legacy technology, which often acts as a bottleneck due to its outdated nature.

What device should be used to run the tests? What operating system version, manufacturer and model to use? How to get these devices? These are some of the questions we ask ourselves when planning a mobile application testing strategy. If we consider automating these tests, other questions will surely come to mind.

Banking, financial service, and insurance (BFSI) applications are complex due to their heavily regulated nature. Beyond the commonly known test cases, QA teams must also consider industry-specific test scenarios to do BFSI testing well. This means that testers must equip themselves with two skill sets: testing knowledge and BFSI domain knowledge.

In the fast-paced and highly regulated world of financial services, software quality is arguably of utmost importance. To ensure financial products meet complex requirements and deliver seamless user experiences, robust testing methods are crucial. One such approach that has gained significant traction in the financial industry is Behavior-Driven Development (BDD) testing.

The DevOps lifecycle is a set of continuous and collaborative practices that bridge the gap between software development and IT operations, promoting seamless integration, automation, and faster delivery of software. The DevOps lifecycle phases include.

AI has reshaped the landscape of almost every industry around the world, and the software testing industry is no exception. The emergence of AI-powered tools for test automation has changed the way QA teams approach their testing activities. This article walks you through the new ways that you can leverage AI testing tools and lists the top 7 best AI testing tools that can bring your testing game to the next level.

Artificial intelligence (AI) has revolutionized the way we do quality assurance (QA). The immense value that AI brings to optimizing testing processes and enhancing efficiency simply can’t be ignored. Organizations that know how to incorporate AI into their testing will gain a strong competitive edge against their competitors.

Interviews are generally anxiety-inducing, but as long as you have some solid preparation before you enter the interview room, there is nothing to worry about. In this article, we have prepared top 60 software testing interview questions, each with detailed answers and explanations, for you to consolidate your knowledge in the field. Our questions are carefully selected and categorized into 3 sections: Beginner level - Intermediate level - Advanced level.

When doing testing automation, it is extremely critical to be able to perform data-driven testing. Data-driven testing (DDT) at the test case level allows you to add one or more data files and manage data binding at the test case level. The most common approach is to provide an Excel or CSV file as input to the test case. The file is modeled per test case structure and then populated with different datasets.