Manual test suites are typically described by natural language, and over time large manual test suites become disordered and harder to use and maintain. This paper focuses on the challenge of providing tool support for refactoring such test suites to make them more usable and maintainable. We describe how we have applied various machine-learning and NLP techniques and other algorithms to the refactoring of manual test suites, plus the tool support we have built to embody these techniques and to allow test suites to be explored and visualised. We evaluate our approach on several industry test suites, and report on the time savings that were obtained.