  Spreadsheets are the most popular end-user programming software, where
formulae act like programs and also have smells. One well recognized common
smell of spreadsheet formulae is nest-IF expressions, which have low
readability and high cognitive cost for users, and are error-prone during reuse
or maintenance. However, end users usually lack essential programming language
knowledge and skills to tackle or even realize the problem. The previous
research work has made very initial attempts in this aspect, while no effective
and automated approach is currently available.
  This paper firstly proposes an AST-based automated approach to systematically
refactoring nest-IF formulae. The general idea is two-fold. First, we detect
and remove logic redundancy on the AST. Second, we identify higher-level
semantics that have been fragmented and scattered, and reassemble the syntax
using concise built-in functions. A comprehensive evaluation has been conducted
against a real-world spreadsheet corpus, which is collected in a leading IT
company for research purpose. The results with over 68,000 spreadsheets with 27
million nest-IF formulae reveal that our approach is able to relieve the smell
of over 99\% of nest-IF formulae. Over 50% of the refactorings have reduced
nesting levels of the nest-IFs by more than a half. In addition, a survey
involving 49 participants indicates that for most cases the participants prefer
the refactored formulae, and agree on that such automated refactoring approach
is necessary and helpful.
