  Use case modeling is very popular to represent the functionality of the
system to be developed, and it consists of two parts: use case diagram and use
case description. Use case descriptions are written in structured natural
language (NL), and the usage of NL can lead to poor descriptions such as
ambiguous, inconsistent and/or incomplete descriptions, etc. Poor descriptions
lead to missing requirements and eliciting incorrect requirements as well as
less comprehensiveness of produced use case models. This paper proposes a
technique to automate detecting bad smells of use case descriptions, symptoms
of poor descriptions. At first, to clarify bad smells, we analyzed existing use
case models to discover poor use case descriptions concretely and developed the
list of bad smells, i.e., a catalogue of bad smells. Some of the bad smells can
be refined into measures using the Goal-Question-Metric paradigm to automate
their detection. The main contribution of this paper is the automated detection
of bad smells. We have implemented an automated smell detector for 22 bad
smells at first and assessed its usefulness by an experiment. As a result, the
first version of our tool got a precision ratio of 0.591 and recall ratio of
0.981.
