Systems | Development | Analytics | API | Testing

Bias in, Bias Out : Knowing various Biases in Testing AI | Maheshwaran VK | Testflix 2025

Just like humans, AI systems are shaped by how they are brought up. In the case of Large Language Models, this upbringing happens through data collection, training, and productization. At each of these stages, bias can quietly enter the system through the data we select, the way models are trained, or the assumptions embedded into the final product. These biases, whether intentional or accidental, influence how models think, respond, and interact with users in the real world.

Why I Switched From Rest Assured To Keploy For Microservices Testing

If you’ve been using Rest Assured for API testing, you know how powerful it is. The syntax looks simple and easier to understand, but things get interesting when you have to write test cases and mocks for a microservices application that has more than 2 services. In this blog, I am exactly sharing my pain of writing Rest Assured test cases for a microservices application and why I switched to Keploy not because I am working here, but to show you the real pain points Keploy solves.

What is AI Governance? 2026 Framework Guide

While AI is revolutionizing the future of nearly every industry, it’s also created a unique set of challenges and liabilities that will need to be addressed as the area grows. Enter AI governance: a set of rules and best practices to ensure that AI is used effectively, securely, and responsibly. But what exactly does that mean, and why is it so crucial for businesses?

Exploring API Endpoints in Depth

API endpoints process requests that power our digital world—from social feeds updating in real-time to enterprise systems syncing global inventories across continents. APIs comprise over 57% of Internet traffic, yet API endpoints often remain misunderstood. This leads to design inconsistencies, brittle integrations, and costly maintenance issues. Sound familiar? If you've merged microservices and untangled mismatched status codes, you know the pain.

Testing Agentic AI | Robert Sabourin | Testflix2025 | #testingcommunity

This talk explores the challenges of testing agentic AI systems—AI that autonomously reacts to events and initiates processes. Drawing on decades of experience, Robert Sabourin emphasizes that testing begins and ends with risk. A three-dimensional model (business impact, technical risk, autonomy) guides evaluation. Testers generate ideas using a broad taxonomy, from capabilities and failure modes to creative and adversarial approaches. Continuous testing and monitoring ensure findings inform business decisions, emphasizing learning over correctness.

Building Quality in LLM-Powered Applications | Craig Risi | Testflix2025 | #testingcommunity

As organizations rapidly adopt Large Language Models, many discover that building reliable and trustworthy AI systems is far more complex than traditional software development. LLMs are non-deterministic, context-sensitive, and prone to issues like bias, hallucinations, and prompt injection, making quality assurance a deeper challenge than simple testing.

Apache HBase ETL Tools: Bulk Load & Incremental Strategies

Apache HBase provides a distributed, column-oriented model with tables → rows → column families/qualifiers and versioned cells. The design is ideal for sparse, wide datasets. ETL is central because performance hinges on how data moves through the default write path—WAL → MemStore → HFiles—versus bulk-load paths that write HFiles directly.

Penetration Testing Services To Strengthen Your Security And Reliability

Modern technology (APIs, Microservices, Cloud Platforms, Mobile Apps) is rapidly changing and becoming a target for attackers as they continue to advance and become more sophisticated. Organizations use penetration testing (a form of ethical hacking) to identify weaknesses in their systems and address them before they can be taken advantage of.

How is Katalon's approach to AI in software testing different?

Katalon’s AI approach is different because it builds on tools teams already use, adds AI without forcing process changes, and introduces novel capabilities like generating tests directly from real user behavior. It also applies AI across the entire testing lifecycle, creating a more complete and unified solution than most tools offer. — Coty Rosenblath, Chief Technology Officer at Katalon Follow Katalon for more insights in our series!

Best 5 Container Image Security Platforms for 2026

By 2026, container image security will no longer be evaluated in isolation. For most organizations, the image layer has become one of the primary sources of security debt, quietly accumulating vulnerabilities that multiply across services, clusters, and environments. What has changed is not just the volume of vulnerabilities, but the cost of managing them. Faster release cycles, shorter maintenance windows, and tighter compliance expectations have pushed teams to reconsider whether traditional scanning-and-patching workflows are sustainable at scale.