Forces that Prevent Agile Adoption in the Automotive Domain

  • Philipp HohlEmail author
  • Jürgen Münch
  • Kurt Schneider
  • Michael Stupperich
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10027)


Context: The current transformation of automotive development towards innovation, permanent learning and adapting to changes are directing various foci on the integration of agile methods. Although, there have been efforts to apply agile methods in the automotive domain for many years, a wide-spread adoption has not yet taken place.

Goal: This study aims to gain a better understanding of the forces that prevent the adoption of agile methods.

Method: Survey based on 16 semi-structured interviews from the automotive domain. The results are analyzed by means of thematic coding.

Results: Forces that prevent agile adoption are mainly of organizational, technical and social nature and address inertia, anxiety and context factors. Key challenges in agile adoption are related to transforming organizational structures and culture, achieving faster software release cycles without loss of quality, the importance of software reuse in combination with agile practices, appropriate quality assurance measures, and the collaboration with suppliers and other disciplines such as mechanics.

Conclusion: Significant challenges are imposed by specific characteristics of the automotive domain such as high quality requirements and many interfaces to surrounding rigid and inflexible processes. Several means are identified that promise to overcome these challenges.


Software development Agile methods Automotive 


  1. 1.
    Manhart, P., Schneider, K.: Breaking the ice for agile development of embedded software: an industry experience report. In: Proceedings of the 26th International Conference on Software Engineering, pp. 378–386, 23–28 May 2004Google Scholar
  2. 2.
    Weber, S.: Agile in Automotive – State of Practice (2015). Accessed 1 Dec. 2015
  3. 3.
    Eliasson, U., Heldal, R., Pelliccione, P., Lantz, J.: Architecting in the automotive domain: descriptive vs prescriptive architecture. In: Bass, L., Lago, P., Kruchten, P. (eds.) 12th Working IEEE/IFIP Conference on Software Architecture, WICSA, pp. 115–118. IEEE, Piscataway (2015)Google Scholar
  4. 4.
    Eliasson, U., Heldal, R., Lantz, J., Berger, C.: Agile model-driven engineering in mechatronic systems - an industrial case study. In: Dingel, J., Schulte, W., Ramos, I., Abrahão, S., Insfran, E. (eds.) MODELS 2014. LNCS, vol. 8767, pp. 433–449. Springer, Heidelberg (2014). doi: 10.1007/978-3-319-11653-2_27 Google Scholar
  5. 5.
    Stelzmann, E., Kreiner, C., Spork, G., Messnarz, R., Koenig, F.: Agility meets systems engineering: a catalogue of success factors from industry practice. In: Riel, A., O’Connor, R., Tichkiewitch, S., Messnarz, R. (eds.) EuroSPI 2010. CCIS, vol. 99, pp. 245–256. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-15666-3_22 CrossRefGoogle Scholar
  6. 6.
    Katumba, B., Knauss, E.: Agile development in automotive software development: challenges and opportunities. In: Jedlitschka, A., Kuvaja, P., Kuhrmann, M., Männistö, T., Münch, J., Raatikainen, M. (eds.) PROFES 2014. LNCS, vol. 8892, pp. 33–47. Springer, Heidelberg (2014). doi: 10.1007/978-3-319-13835-0_3 Google Scholar
  7. 7.
    Dresch, A., Lacerda, D.P., Antunes, J.A.V.: Design Science Research. Springer International Publishing, Cham (2015)CrossRefGoogle Scholar
  8. 8.
    Runeson, P., Hst, M.: Guidelines for conducting and reporting case study research in software engineering. Empirical Softw. Eng. 14(2), 131–164 (2009)CrossRefGoogle Scholar
  9. 9.
    Bryman, A.: Social Research Methods, 2nd edn. Univ. Press, Oxford (2004)Google Scholar
  10. 10.
    Easterbrook, S., Singer, J., Storey, M.-A., Damian, D.: Selecting empirical methods for software engineering research. In: Shull, F., Singer, J., Sjberg, D.I.K. (eds.) Guide to Advanced Empirical Software Engineering, pp. 285–311. Springer-Verlag, London Limited, London (2008)CrossRefGoogle Scholar
  11. 11.
    Ritchie, J. (ed.): Qualitative Research Practice: A Guide for Social Science Students and Researchers. Sage, repr ed., Los Angeles (2011)Google Scholar
  12. 12.
    Stol, K.-J., Ralph, P., Fitzgerald, B.: Grounded theory in software engineering research. In: Dillon, L., Visser, W., Williams, L. (eds.) Proceedings of the 38th International Conference on Software Engineering, pp. 120–131 (2016)Google Scholar
  13. 13.
    Corbin, J., Strauss, A.: Grounded theory research: procedures, canons and evaluative criteria. Qual. Sociol. 13, 3–21 (1990)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Philipp Hohl
    • 1
    Email author
  • Jürgen Münch
    • 2
    • 3
  • Kurt Schneider
    • 4
  • Michael Stupperich
    • 1
  1. 1.Daimler AG, Research and DevelopmentUlmGermany
  2. 2.Reutlingen UniversityReutlingenGermany
  3. 3.University of HelsinkiHelsinkiFinland
  4. 4.Leibniz Universität HannoverHannoverGermany

Personalised recommendations