Automated Smell Detection and Recommendation in Natural Language Requirements

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) in writing requirements, which can result in project failures. To
address this issue, we present a tool, named Paska, that automatically detects
quality problems as smells in NL requirements and offers recommendations to
improve their quality. Our approach relies on natural language processing (NLP)
techniques and, most importantly, 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 (precision of 89% and recall of 89%) and
suggesting appropriate Rimay pattern recommendations (precision of 96% and
recall of 94%).