Transforming tomorrow with AI and automation today.

  Imagine: you’re working on a banking system that processes loan applications. It’s trained to decide whether to approve or reject it based on several factors, such as income, credit score, existing debts, and employment status. Now, what if a loan applicant has a high income but a low credit score? They seem to have a stable job but a history of missing payments frequently. It could happen, right? Before you know it, the number of such possible scenarios quickly multiplies and it becomes a hassle to test all conditions on your banking system properly. This is where decision table testing comes in. It ensures you cover all edge cases, minimize oversight, and improve software reliability. It provides you a clear, visual way to understand how different conditions interact — making it easier to catch defects before they impact real users. If you’re ready to remove guesswork from your testing, you must read on. We’ll discuss everything you need to know about decision table testing in ...

  As competition among software companies intensifies and with users expecting top quality in every app they download, it becomes crucial to make test automation an integral part of the development process. Undoubtedly, automation testing is more efficient and reliable than manual testing, allowing teams to ensure the best functionality and user experience. The automation test market is forecast to reach USD 55.2 Billion by 2028, as per MarketsandMarkets, proving how  test automation  has turned into the most adopted process in recent years. Though test automation is far more efficient and quicker than manual testing methods, it often poses challenges that often come in the way of efficient testing. This article will explore the common obstacles testing teams face when automating tests and will attempt to provide practical solutions to overcome them. Common Test Automation Challenges With Suggested Solutions To Overcome Them 1. Lack of a proper test infrastructure Having ...

  decade ago, automated testing emerged as a fixture in the software testing landscape. The Agile methodology had become the undisputed priority for testing teams, just as CI/CD pipelines had for continuous testing and feedback. At the time, implementing automation testing was considered a risky, complicated, and expensive decision. However, its efficacy was impossible to ignore, and it soon came to dominate QA processes across teams. Cut to 2025, and AI has become a similarly emergent technology. Its advantages are quickly becoming apparent in every domain. However, just as with automated testing a decade ago, many CTOs are unsure about the new technology and its viability in driving business value in both the short and long term. CTOs hold a key role in innovating and helping test teams thrive in a competitive future. Here’s why they should prioritize AI in that transformation. Challenges in modern automation testing The easiest way to identify the necessity of AI is to explore t...