  Requirement specifications are typically written in natural language (NL) due
to its usability across multiple domains and understandability by all
stakeholders. However, unstructured NL is prone to quality problems (e.g.,
ambiguity) when writing requirements, which can result in project failures. To
address this issue, we present a tool, named Paska, that takes as input any NL
requirements, automatically detects quality problems as smells in the
requirements, and offers recommendations to improve their quality. Our approach
relies on natural language processing (NLP) techniques and a state-of-the-art
controlled natural language (CNL) for requirements (Rimay), to detect smells
and suggest recommendations using patterns defined in Rimay to improve
requirement quality. We evaluated Paska through an industrial case study in the
financial domain involving 13 systems and 2725 annotated requirements. The
results show that our tool is accurate in detecting smells (89% precision and
recall) and suggesting appropriate Rimay pattern recommendations (96% precision
and 94% recall).
