Was watching Rahul talking to introduce his presentation and I was like: Oh my God! I'm so happy and proud to have Rahul as the first Speaker ever in MoT Sousse Meetup ^^ This could be read on my ...

Prompt injection is a security attack that happens when someone intentionally manipulates the input to a Generative AI system like a chatbot or code generator to make it behave in ways the designer didn’t intend.It’s done by crafting inputs to Gen AI systems in order to confuse, hijack, or redirect the AI’s response by messing with its underlying structure.For software testers, it's a way to test for input attacks on LLM-based systems. Just like a SQL injection or XSS, but here the payload is language and words designed to interfere with the model or system prompts.

Artefacts or records created or collected during the testing process. It serves as proof of what was tested and can be used for further investigation, documentation, or compliance needs.

Build quality into every step of the software lifecycle with the whole team involved

How can AI be your trused advisory board. Rahul takes a long standing leadership model of advisors offering ideas to a leader who makes the final decision and brings it into an AI future

66% of us seem to think so

A model to guide software testers in selecting the right LLM based on task complexity and output quality needs.

When you ask a tool to generate code, your role is central. Every step you take lays the foundation for the final output.

Boilerplate code is the recurring, and reusable code that underpins software projects. It serves as a simple and reliable standard blueprint, that is often repeated across different tests (or test scripts).This familiar code can cut down on rewriting the same routine logic time and again, providing a solid foundation on which the actual test scripting can grow.Example: In a typical test automation framework, you’ll find boilerplate code for handling setup and teardown routines, logging, driver management, and sometimes even error management. Pitfalls with boilerplate code? Overusing boilerplate code can lead to inflexible, and complex code structure. What begins as a time-saving tool may evolve into a maintenance challenge if not used carefully. Copying and pasting the same code everywhere, instead of encapsulating it in reusable methods or modules, can create a hidden trap in long run.

A digital twin is a highly consistent, software-based replica of a physical system that runs the same software, behaves like the real device, and can be interacted with as if it was the real thing. Unlike traditional simulators that mock specific parts or APIs, a digital twin mirrors the actual device’s environment, allowing for realistic and early testing even before the hardware exists. It’s like having a stand-by for your device that you can test, break, and explore without needing the physical hardware on hand.

Test artefacts are key documents or materials generated throughout the software testing process. Test artefacts act as records or evidence for testing activities, starting from the planning phase and to the delivery of a testing project. Artefacts are essential for ensuring transparency, communication and consistency, ensuring that testing efforts align with the overall project objectives. Most artefacts, like test plans, test cases, and traceability matrices, work best when treated as living documentation. This means they’re regularly updated to stay in sync with any changes in project requirements, developments, or test results. Keeping them updated and live ensures that they stay useful. It’s also important to regularly review these artefacts. Periodic formal reviews alongside small changes as needed make sure everything is up-to-date, accurate, and aligned with the current project needs. It also helps to spot any gaps or inconsistencies.