In the past, developers reviewed thousands of lines of code or used SAST (Static Application Security Testing) or DAST (Dynamic Application Security Testing) Tools to discover vulnerabilities. Today, AI Agents detect and fix these issues behind the scenes during the software development process, which is a tremendous improvement. For new systems, this works well, but the problem arises when we consider AI-detected vulnerabilities in operating systems or other critical busi

In a randomized controlled trial, METR invited developers to complete 246 tasks. Despite believing AI improved their speed by 20%, these developers actually took 19% longer to finish tasks when using AI. AI helps with specific tasks, such as pattern matching and report summarization, but in areas like performance engineering, experienced engineers can outperform AI due to their specialization and intuition. This highlights situations where human expertise still surpasses AI i

Test automation enables faster releases, lower costs, and better quality across web, mobile, and API software delivery.

Artificial intelligence is revolutionizing our world. Performance engineering and Machine Learning experts work hard behind the scenes.

Generative AI, a versatile form of artificial intelligence, is not limited to data analysis.

In today’s fast-paced world, software must run smoothly and quickly, with no exceptions.

Let's explore some real-world applications to illustrate the impact of AI and ML on performance engineering.

Let's understand use cases and challenges from a Performance Engineering perspective.

Performance engineering for LLMs involves evaluating their efficiency, accuracy, and scalability

Artificial Intelligence (AI) is like giving machines a brain to think and learn like humans. AI can tackle many tasks, making life easier.