Systems | Development | Analytics | API | Testing

Cigniti is a global digital assurance and engineering company that manages and implements enterprise digital transformation projects. One of its premier clients, a large cruise line headquartered in Florida, rolled out a fleet of the three most modern ships in the world. The cruise line commissioned Cigniti to develop web, mobile, and tablet automation supporting the back office, onboard staff, and customers.

Database testing is the process of evaluating the accuracy, reliability, and performance of a database system. Its purpose is to ensure that the data stored there is consistent, valid, and can be correctly manipulated for business needs. The components to be tested are usually database schema, tables, and database triggers. Testers leverage SQL queries, data comparison tools, automation frameworks, or load testing tools to examine the data integrity, validity, security, performance, and structure.

There is no doubt about it: Artificial Intelligence (AI) and Machine Learning (ML) has changed the way we think about software testing. Ever since the introduction of the disruptive AI-powered language model ChatGPT, a wide range of AI-augmented technologies have also emerged, and the benefits they brought surely can’t be ignored. In this article, we will guide you to leverage AI/ML in software testing to bring your QA game to the next level.

In the complex and rapidly evolving landscape of software development, the ability to make swift, well-informed decisions can be the dividing line between success and missed opportunity. As development cycles shrink and user demands grow, navigating the waters of software testing requires not just precision, but also clarity. This is where Katalon TestOps steps in with its game-changing feature: Virtual Data Analyst (VDA), powered by GPT technology.

We live in a highly interconnected world, and that interconnectedness couldn’t have existed without the tremendous contributions from the telecommunications industry. With 5G technology finally becoming mainstream, IoT growing at a rapid rate, and AI tech gaining prominence, the telecom industry is surely up for an exciting future. This faster-than-ever growth comes with the need for robust, continuous telecom testing to always maintain a high standard of service quality.

Application programming interfaces (APIs) serve as the backbone of today’s digital economy. They connect applications and web services with each other to exchange information and deliver thousands of digital experiences to users every day. With such significance, APIs can’t afford to be buggy, and software testers must frequently test them to identify and address potential issues. However, there’s a catch. Sometimes an API is not fully developed, or is simply too expensive to access.

API integration allows different software systems to connect with each other and exchange data seamlessly, and this process is literally the backbone of our digital world. While it is entirely possible to perform API integration manually, using dedicated API integration tools offer so many advantages to make this process more efficient, reliable, and manageable.

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.