  Context: Linear Temporal Logic (LTL) has been used widely in verification.
Its importance and popularity have only grown with the revival of temporal
logic synthesis, and with new uses of LTL in robotics and planning activities.
All these uses demand that the user have a clear understanding of what an LTL
specification means.
  Inquiry: Despite the growing use of LTL, no studies have investigated the
misconceptions users actually have in understanding LTL formulas. This paper
addresses the gap with a first study of LTL misconceptions. Approach: We study
researchers' and learners' understanding of LTL in four rounds (three written
surveys, one talk-aloud) spread across a two-year timeframe. Concretely, we
decompose "understanding LTL" into three questions. A person reading a spec
needs to understand what it is saying, so we study the mapping from LTL to
English. A person writing a spec needs to go in the other direction, so we
study English to LTL. However, misconceptions could arise from two sources: a
misunderstanding of LTL's syntax or of its underlying semantics. Therefore, we
also study the relationship between formulas and specific traces.
  Knowledge: We find several misconceptions that have consequences for
learners, tool builders, and designers of new property languages. These
findings are already resulting in changes to the Alloy modeling language. We
also find that the English to LTL direction was the most common source of
errors; unfortunately, this is the critical "authoring" direction in which a
subtle mistake can lead to a faulty system. We contribute study instruments
that are useful for training learners (whether academic or industrial) who are
getting acquainted with LTL, and we provide a code book to assist in the
analysis of responses to similar-style questions.
  Grounding: Our findings are grounded in the responses to our survey rounds.
Round 1 used Quizius to identify misconceptions among learners in a way that
reduces the threat of expert blind spots. Rounds 2 and 3 confirm that both
additional learners and researchers (who work in formal methods, robotics, and
related fields) make similar errors. Round 4 adds deep support for our
misconceptions via talk-aloud surveys.
  Importance This work provides useful answers to two critical but unexplored
questions: in what ways is LTL tricky and what can be done about it? Our survey
instruments can serve as a starting point for other studies.
