  Spreadsheets (sometimes also called Excel programs) are powerful tools which
play a business-critical role in many organizations. However, due to faulty
spreadsheets many bad decisions have been taken in recent years. Since then, a
number of researchers have been studying spreadsheet errors. However, one issue
that hinders discussion among researchers and professionals is the lack of a
commonly accepted taxonomy.
  Albeit a number of taxonomies for spreadsheet errors have been proposed in
previous work, a major issue is that they use the term error that itself is
already ambiguous. Furthermore, to apply most existing taxonomies, detailed
knowledge about the underlying process and knowledge about the "brain state" of
the acting spreadsheet users is required. Due to these limitations, known
error-like phenomena in freely available spreadsheet corpora cannot be
classified with these taxonomies.
  We propose Asheetoxy, a simple and phenomenon-oriented taxonomy that avoids
the problematic term error altogether. An initial study with 7 participants
indicates that even non-spreadsheet researchers similarly classify real-world
spreadsheet phenomena using Asheetoxy.
