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@PHDTHESIS{Kugler:1010169,
      author       = {Kugler, Alexander},
      othercontributors = {Kowalewski, Stefan and Rumpe, Bernhard},
      title        = {{T}est case generation from natural language requirements
                      for embedded systems with semantic role labeling},
      volume       = {2025,04},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {RWTH Aachen University, Department of Computer Science},
      reportid     = {RWTH-2025-03901},
      series       = {Aachener Informatik-Berichte},
      pages        = {1 Online-Ressource : Illustrationen},
      year         = {2025},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University; Dissertation, RWTH Aachen University, 2025},
      abstract     = {The work presented in this thesis explores the application
                      of Semantic Role Labeling(SRL) for the generation of test
                      cases from natural language requirements for embedded
                      systems. The approach, labelled Test Generation with
                      Semantic Role Labeling (TG-SRL), is composed of five stages
                      and combines machine learning with a rule-based approach.
                      Information extracted via Semantic Role Labeling (SRL) is
                      initially aggregated into logical expressions before being
                      translated into First-Order Logic (FOL) formulae. Test case
                      generation is achieved using Satisfiability Modulo Theory
                      (SMT) solving. By modifying the SMT instance according to
                      defined tactics, a test suite is generated.The thesis
                      concludes with an evaluation of TG-SRL using a mutant-based
                      strength analysis, and a comparison with the Nat2Test
                      approach from Carvalho et al. TG-SRL performs favorably and
                      provides valuable insights into employing Natural Language
                      Processing (NLP) methods, and in particular SRL, in the
                      field of test case generation. The methods and concepts
                      presented in this thesis have been implemented in a publicly
                      available research framework.},
      cin          = {122810 / 120000},
      ddc          = {004},
      cid          = {$I:(DE-82)122810_20140620$ / $I:(DE-82)120000_20140620$},
      typ          = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
      doi          = {10.18154/RWTH-2025-03901},
      url          = {https://publications.rwth-aachen.de/record/1010169},
}