Systems | Development | Analytics | API | Testing

Discover a Bug Reporting Blueprint with 15 essential templates for QA Testing Workflow in 2023. Boost efficiency and streamline your software testing!

When npm announced that version 12 would require developers to explicitly approve install scripts, the reaction across the community was almost universally positive. For years, security researchers had warned that package installation had become one of the weakest links in the JavaScript supply chain. Every npm install represented an opportunity for third-party code to execute automatically, often before developers had even looked at the source they were downloading.

Sometimes, comments are not specifically contextual to an element, but rather to your entire webpage. This is why we launched floating comments!

Choosing an enterprise test management platform is an architecture decision, not just a feature checklist. That choice comes down to how the platform stores data, how deeply testing connects to development, how far reporting and traceability extend, and how much the platform can absorb as testing volume, automation, and compliance requirements grow.

The swift introduction of Large Language Models (LLMs) and autonomous agents to business software ecosystems has posed a fundamental security issue: the unification of code and data. Traditional software designs carefully separate user input from operational commands. LLMs, on the other hand, are fed system commands, retrieved documents, user communications, and third-party API answers all in one context window.

Discover how BigQuery Graph with measures grounds autonomous AI agents in unified enterprise truth, enabling precise reasoning across complex data relationships.

The request timed out after five seconds. The application said context deadline exceeded. The dependency was healthy, its dashboards were green, and its own latency histogram showed nothing above 30 milliseconds.

Synthetic monitoring has been a critical part of application reliability for years. It gives engineering and operations teams a way to proactively test applications, APIs, and critical customer journeys before users encounter problems. But there is a fundamental limitation with the traditional approach: Someone has to create the tests. As applications become more distributed and customer journeys become more complex, organizations can end up maintaining hundreds or even thousands of synthetic scripts.

Katalon's library of testing skills for AI agents holds 13 skills. Building it produces 124 files. The 13 skills are the actual work: how to plan a test cycle, how to design cases from a requirement, how to read a failing run and decide whether a release is safe. The other 111 files are packaging. Four of them arrived this month and they are the subject of this post.