  It is commonly accepted that the quality of requirements specifications
impacts subsequent software engineering activities. However, we still lack
empirical evidence to support organizations in deciding whether their
requirements are good enough or impede subsequent activities. We aim to
contribute empirical evidence to the effect that requirements quality defects
have on a software engineering activity that depends on this requirement. We
conduct a controlled experiment in which 25 participants from industry and
university generate domain models from four natural language requirements
containing different quality defects. We evaluate the resulting models using
both frequentist and Bayesian data analysis. Contrary to our expectations, our
results show that the use of passive voice only has a minor impact on the
resulting domain models. The use of ambiguous pronouns, however, shows a strong
effect on various properties of the resulting domain models. Most notably,
ambiguous pronouns lead to incorrect associations in domain models. Despite
being equally advised against by literature and frequentist methods, the
Bayesian data analysis shows that the two investigated quality defects have
vastly different impacts on software engineering activities and, hence, deserve
different levels of attention. Our employed method can be further utilized by
researchers to improve reliable, detailed empirical evidence on requirements
quality.
