On identifying possible artificial intelligence applications in requirements engineering processes

Forschung im Ingenieurwesen, vol. 87, pp. 497–506

Abstract

Abstract Managing an ever-growing number of requirements is a time consuming and highly complex activity in product development projects. With the rise of Artificial Intelligence (AI) in the last decades, algorithms are now capable of supporting Requirements Engineering (RE) tasks. The algorithm’s ability to intelligently process natural language data as well as the application in RE have been published extensively. In established RE processes however, it is often unclear, where AI algorithms can be utilized. The result is a significant imbalance between state-of-the-art AI algorithms and their application rates in real RE processes. One of the reasons is, that current models of RE processes aren’t able to convey the needed information for the identification of suitable tasks. Therefore, this paper aims to provide a framework of finite and standardized process steps with a data processing-oriented perspective, that can be used to systematically identify the points in the RE processes where AI algorithms can be applied. With the standardized process steps of this framework, established RE processes can be abstracted and expressed in a way that is compatible with the scope of AI algorithms. Due to the finite number of standardized process steps, libraries of suitable AI algorithms for the defined process steps can be built, so that their applicability can be efficiently assessed. The presented framework was developed collaboratively during a research project with industry participation.

Authors 9

1. Simon Dehn corresponding Aachen Institute for Machine Elements and Systems Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Machine Elements and Systems Engineering, Schinkelstr. 10, 52062, Aachen, Germany

   Institute for Machine Elements and Systems Engineering, Schinkelstr. 10, 52062, Aachen, Germany

2. Georg Jacobs Aachen Institute for Machine Elements and Systems Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Machine Elements and Systems Engineering, Schinkelstr. 10, 52062, Aachen, Germany

   Institute for Machine Elements and Systems Engineering, Schinkelstr. 10, 52062, Aachen, Germany

3. Thilo Zerwas Aachen Institute for Machine Elements and Systems Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Machine Elements and Systems Engineering, Schinkelstr. 10, 52062, Aachen, Germany

   Institute for Machine Elements and Systems Engineering, Schinkelstr. 10, 52062, Aachen, Germany

4. Joerg Berroth Aachen Institute for Machine Elements and Systems Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Machine Elements and Systems Engineering, Schinkelstr. 10, 52062, Aachen, Germany

   Institute for Machine Elements and Systems Engineering, Schinkelstr. 10, 52062, Aachen, Germany

5. Matthis Hötter Aachen Institute for Automotive Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Automotive Engineering, Steinbachstr. 7, 52074, Aachen, Germany

   Institute for Automotive Engineering, Steinbachstr. 7, 52074, Aachen, Germany

6. Matthias Korten Aachen Institute for Automotive Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Automotive Engineering, Steinbachstr. 7, 52074, Aachen, Germany

   Institute for Automotive Engineering, Steinbachstr. 7, 52074, Aachen, Germany

7. Marvin S. Müller
   Affiliation as printed

   PEM Motion GmbH, Bohr 12, 52072, Aachen, Germany

   PEM Motion GmbH, Bohr 12, 52072, Aachen, Germany

8. Nico Gossen
   Affiliation as printed

   PEM Motion GmbH, Bohr 12, 52072, Aachen, Germany

   PEM Motion GmbH, Bohr 12, 52072, Aachen, Germany

9. Serena Striegel

   BMW (Germany)

   Affiliation as printed

   BMW AG, Petuelring 130, 80788, Munich, Germany

   BMW AG, Petuelring 130, 80788, Munich, Germany