Autonomous AI for Enterprise Testing at Scale

Unit testing is one of the biggest productivity killers in modern Java development. Developers report spending most of their time on tasks other than writing code. Many devs even skip writing unit tests altogether. But what if AI could eliminate 95% of that burden?

Developer toil is real — days, even weeks, are spent conducting manual, repetitive, timegobbling tasks like unit testing, which slows enterprises down. And while GenAI coding assistants can support developers, they repeatedly fall short in performing complex unit testing tasks without human oversight.

The problem is compounded by the shortage of experienced testers, leaving developers drowning in unit testing rather than writing and innovating application code. Reports show 75% of developers’ time is spent on tasks other than code generation, resulting in 57% of developers admitting to skipping unit tests entirely.

For companies to succeed, code must be fool proof. So how can enterprises ensure testing is comprehensive and done with due diligence — and at scale?

The answer lies in reinforcement learningbased autonomous AI, which can eliminate up to 95% of the time developers typically spend on test writing. In this session, you will learn how.

Thunder Talk: Making Sense of Embeddings for Java Developers – LangChain4j – Part 2

Embeddings are the backbone of modern AI.  They turn words, code, and other data types into single-precision floating-point vectors that machines can handle. In this 20-minute ThunderTalk [insert loud-thunder.mp3 here], Frank will explain what embeddings are, why they matter, and how to use them for search,​ recommendations, and RAG-based chatbots.  We’ll use LC4J for a couple of quick demos.