  Requirements elicitation is an important phase of any software project: the
errors in requirements are more expensive to fix than the errors introduced at
later stages of software life cycle. Nevertheless, many projects do not devote
sufficient time to requirements. Automated requirements generation can improve
the quality of software projects. In this article we have accomplished the
first step of the research on this topic: we have applied the vanilla sentence
autoencoder to the sentence generation task and evaluated its performance. The
generated sentences are not plausible English and contain only a few meaningful
words. We believe that applying the model to a larger dataset may produce
significantly better results. Further research is needed to improve the quality
of generated data.
