What use are AI for code tools to developers if they have low levels of accuracy, test coverage and are non-deterministic in their outcomes?
In the fast-evolving landscape of software development, AI-driven tools promise to revolutionize unit testing by enhancing speed, accuracy, and coverage. Yet, many developers remain skeptical, questioning the utility of AI-for-code solutions plagued by low accuracy, inadequate test coverage, and non-deterministic behavior. This presentation explores the transformative potential of Diffblue Cover, an agentic AI solution designed to overcome these challenges.
Unlike LLM-driven generative AI coding assistants, learn how advanced AI Agents can use reinforcement learning to autonomously generate reliable, deterministic unit tests that are guaranteed to compile and run correctly every time.
By addressing critical developer concerns—such as trustworthiness, scalability, and integration into CI/CD pipelines. As an AI Diffblue Cover redefines unit test generation for Java codebases.
The talk will delve into the pitfalls of conventional AI tools and demonstrate how agentic AI can deliver higher test coverage, faster development cycles, and reduced manual effort without compromising reliability.
Join us as we pose the question: What use are AI tools if they fail to meet developers' expectations for accuracy and consistency?
In the fast-evolving landscape of software development, AI-driven tools promise to revolutionize unit testing by enhancing speed, accuracy, and coverage. Yet, many developers remain skeptical, questioning the utility of AI-for-code solutions plagued by low accuracy, inadequate test coverage, and non-deterministic behavior. This presentation explores the transformative potential of Diffblue Cover, an agentic AI solution designed to overcome these challenges.
Unlike LLM-driven generative AI coding assistants, learn how advanced AI Agents can use reinforcement learning to autonomously generate reliable, deterministic unit tests that are guaranteed to compile and run correctly every time.
By addressing critical developer concerns—such as trustworthiness, scalability, and integration into CI/CD pipelines. As an AI Diffblue Cover redefines unit test generation for Java codebases.
The talk will delve into the pitfalls of conventional AI tools and demonstrate how agentic AI can deliver higher test coverage, faster development cycles, and reduced manual effort without compromising reliability.
Join us as we pose the question: What use are AI tools if they fail to meet developers' expectations for accuracy and consistency?
Paul Crane
Diffblue
Paul Crane is a Senior Architect at Diffblue. With a passion for solving complex problems, and pushing technical boundaries he has many years experience of developing software solutions for small companies, governmental departments, and university research laboratories. He holds a PhD in Computer Science from the University of Otago, New Zealand.