  In this paper we address the following question: How can we use Large
Language Models (LLMs) to improve code independently of a human, while ensuring
that the improved code
  - does not regress the properties of the original code?
  - improves the original in a verifiable and measurable way?
  To address this question, we advocate Assured LLM-Based Software Engineering;
a generate-and-test approach, inspired by Genetic Improvement. Assured LLMSE
applies a series of semantic filters that discard code that fails to meet these
twin guarantees. This overcomes the potential problem of LLM's propensity to
hallucinate. It allows us to generate code using LLMs, independently of any
human. The human plays the role only of final code reviewer, as they would do
with code generated by other human engineers.
  This paper is an outline of the content of the keynote by Mark Harman at the
International Workshop on Interpretability, Robustness, and Benchmarking in
Neural Software Engineering, Monday 15th April 2024, Lisbon, Portugal.
