  Context and Motivation: Natural language is the most common form to specify
requirements in industry. The quality of the specification depends on the
capability of the writer to formulate requirements aimed at different
stakeholders: they are an expression of the customer's needs that are used by
analysts, designers and testers. Given this central role of requirements as a
mean to communicate intention, assuring their quality is essential to reduce
misunderstandings that lead to potential waste. Problem: Quality assurance of
requirement specifications is largely a manual effort that requires expertise
and domain knowledge. However, this demanding cognitive process is also
congested by trivial quality issues that should not occur in the first place.
Principal ideas: We propose a taxonomy of requirements quality assurance
complexity that characterizes cognitive load of verifying a quality aspect from
the human perspective, and automation complexity and accuracy from the machine
perspective. Contribution: Once this taxonomy is realized and validated, it can
serve as the basis for a decision framework of automated requirements quality
assurance support.
