Systems | Development | Analytics | API | Testing

For QA professionals in financial services, the pressure is unlike any other industry. Whether you’re supporting a multinational bank or a regional credit union, one thing remains the same: even the smallest software error can have massive consequences. A glitch in an online payment system could affect thousands of customers. A regression bug in account processing might trigger a compliance breach.

Millions of users rushed to ChatGPT overnight, all craving Studio Ghibli-style art. What started as a fun trend quickly went viral, pushing OpenAI’s servers to their limits. The "Ghibli Trend" wasn't just another online craze — it became a live performance and load-testing scenario for OpenAI. Social media users began sharing Ghibli-inspired AI images, creating a massive buzz.

Despite fears of job loss, QA professionals are leaning into AI faster than ever, according to Katalon’s newly released 2025 State of Software Quality Report. The report reveals that testers using AI tools are twice as likely to fear being replaced by them, a paradox that underscores the profession’s evolving relationship with automation.

Teams often find themselves managing a big number of test cases, many of which are nearly identical except for variations in input data. This redundancy leads to a higher risk of missing critical test coverage. Test parameterization solves this challenge by allowing testers to re-run the same test logic with multiple data iterations - teams define parameters within a single test script and execute it dynamically via a dataset with different values.

Large language models (LLMs) have redefined artificial intelligence (AI), pushing the boundaries of natural language processing (NLP) and enabling machines to understand, generate, and manipulate human-like text. From chatbots and content creation to legal and medical applications, LLMs are transforming industries at an unprecedented pace. But what makes these models so powerful? How do they work? And what challenges do they pose?

Worldwide, over 150,000 companies rely on Salesforce for business-critical operations such as sales, marketing, or customer service. Given its critical role in business success, ensuring that Salesforce applications function correctly is not just a best practice — it’s a necessity. However, Salesforce environments are highly dynamic. Frequent updates, extensive customizations, and numerous third-party integrations mean that changes can introduce unexpected issues.

Generally, in today’s fast-moving development cycle, software reliability and system performance rely on successful code testing. Programmers depend on code tester tools to accomplish pre-deployment code debugging, validation, and optimization. The correct selection of code testing tools brings substantial workflow improvements to developers working with web development, software engineering, or mobile applications.

Article four in the “Quality Engineering for CEOs” series brings you an exploration of the role of software quality in organizational confidence.