Systems | Development | Analytics | API | Testing

2024 has been a transformative year for Katalon, filled with key updates and features designed to address the evolving needs of testers and teams worldwide. With 34 major releases across our suite of products, we’ve focused on making testing faster, smarter, and more efficient for a broad range of use cases. Here’s a look back at the most significant and impactful updates from 2024 to ensure you’re equipped for success in your testing journey.

Will developers and testers always be stuck advocating for quality, or can the technical and business sides ever connect?

Access to accurate, timely financial data is essential for finance professionals navigating the complexities of today’s dynamic business environment. In a world where business decisions need to be made quickly and confidently, having reliable data at your fingertips can make all the difference. Yet, achieving this level of insight is easier said than done.

Self-service financial reporting solution provides a new era of unmatched ease, flexibility, and accuracy for Microsoft Dynamics users.

Quality Assurance (QA) ensures that products meet defined standards and function as intended. As businesses grow and software becomes more complex, the need for scaling QA processes becomes even more noticeable. On the other hand, reporting helps track the progress of testing efforts, identifies defects, and measures the effectiveness of test strategies. Without proper reporting, it's challenging to maintain control over quality, especially as projects grow.

Production traffic can often be unpredictable, and distinguishing genuine user interactions from mere noise becomes a pivotal step in comprehensively grasping the types of requests and workflows occurring within your deployment. One important concept to explore in this context is shadow traffic, which plays a significant role in analytics and cybersecurity but is often misunderstood or rarely discussed.

It’s that time of year again—performance review season. Reviewing impact statements across various crafts, I’ve noticed a pattern among Quality Engineering (QE) professionals. Statements often focus on test cases, defects found, and deadlines met. But is that truly all we bring to the table? I believe there’s a much broader, often undervalued, impact on the QE role.

Generative AI’s potential to drive innovation, improve efficiency and create competitive advantages is enormous. However, the ability to fully realize the benefits of generative AI hinges on one crucial factor: data strategy. “Data Strategies for AI Leaders,” a report co-written by MIT and Snowflake, underscores how organizations must invest in robust data foundations to succeed in the AI era.

One of the software industry’s most important pain points in testing is “Coverage”. It is manually impossible to generate test cases while covering edge cases and different scenarios with the scale of how much code gets written each day. AI Agents here save time and improve test coverage and reliability. Developers can now focus on core logic while AI handles repetitive and error-prone tasks like writing unit tests.

Object-oriented programming is a programming model that designs software around objects and data, as opposed to procedures and logic. Generally, object-oriented programming delivers more reusable, maintainable, and scalable code. This is in no small part due to its core concepts like inheritance, abstraction, and encapsulation. As we explained before, encapsulation involves combining data and the methods that operate on it into one unit, usually a class.