  Background: The detection and extraction of causality from natural language
sentences have shown great potential in various fields of application. The
field of requirements engineering is eligible for multiple reasons: (1)
requirements artifacts are primarily written in natural language, (2) causal
sentences convey essential context about the subject of requirements, and (3)
extracted and formalized causality relations are usable for a (semi-)automatic
translation into further artifacts, such as test cases. Objective: We aim at
understanding the value of interactive causality extraction based on syntactic
criteria for the context of requirements engineering. Method: We developed a
prototype of a system for automatic causality extraction and evaluate it by
applying it to a set of publicly available requirements artifacts, determining
whether the automatic extraction reduces the manual effort of requirements
formalization. Result: During the evaluation we analyzed 4457 natural language
sentences from 18 requirements documents, 558 of which were causal (12.52%).
The best evaluation of a requirements document provided an automatic extraction
of 48.57% cause-effect graphs on average, which demonstrates the feasibility of
the approach. Limitation: The feasibility of the approach has been proven in
theory but lacks exploration of being scaled up for practical use. Evaluating
the applicability of the automatic causality extraction for a requirements
engineer is left for future research. Conclusion: A syntactic approach for
causality extraction is viable for the context of requirements engineering and
can aid a pipeline towards an automatic generation of further artifacts from
requirements artifacts.
