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[Context:] Model-based testing is an instrument for automated generation oftest cases. It requires identifying requirements in documents, understandingthem syntactically and semantically, and then translating them into a testmodel. One light-weight language for these test models are Cause-Effect-Graphs(CEG) that can be used to derive test cases. [Problem:] The creation of testmodels is laborious and we lack an automated solution that covers the entireprocess from requirement detection to test model creation. In addition, themajority of requirements is expressed in natural language (NL), which is hardto translate to test models automatically. [Principal Idea:] We build on thefact that not all NL requirements are equally unstructured. We found that 14 %of the lines in requirements documents of our industry partner contain"pseudo-code"-like descriptions of business rules. We apply Machine Learning toidentify such semi-structured requirements descriptions and propose arule-based approach for their translation into CEGs. [Contribution:] We makethree contributions: (1) an algorithm for the automatic detection ofsemi-structured requirements descriptions in documents, (2) an algorithm forthe automatic translation of the identified requirements into a CEG and (3) astudy demonstrating that our proposed solution leads to 86 % time savings fortest model creation without loss of quality.

Automated Generation of Test Models from Semi-Structured Requirements