Towards Integrating Data-Driven Requirements Engineering into the Software Development Process: A Vision Paper

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12045)


[Context and motivation] Modern software engineering processes have shifted from traditional upfront requirements engineering (RE) to a more continuous way of conducting RE, particularly including data-driven approaches. [Question/problem] However, current research on data-driven RE focuses more on leveraging certain techniques such as natural language processing or machine learning than on making the concept fit for facilitating its use in the entire software development process. [Principal ideas/results] In this paper, we propose a research agenda composed of six distinct research directions. These include a data-driven RE infrastructure, embracing data heterogeneity, context-aware adaptation, data analysis and decision support, privacy and confidentiality, and finally process integration. Each of these directions addresses challenges that impede the broader use of data-driven RE. [Contribution] For researchers, our research agenda provides topics relevant to investigate. For practitioners, overcoming the underlying challenges with the help of the proposed research will allow to adopt a data-driven RE approach and facilitate its seamless integration into modern software engineering. For users, the proposed research will enable the transparency, control, and security needed to trust software systems and software providers.


Data-driven requirements engineering Feedback gathering Requirements monitoring Model-driven Engineering 



This work has been supported by: the Spanish project GENESIS (TIN2016-79269-R), the Christian Doppler Forschungsgesellschaft, the Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology and Development, and the Austrian Science Fund (FWF) under the grant numbers J3998-N31, P28519-N31, and P30525-N31.


  1. 1.
    Brambilla, M., Cabot, J., Wimmer, M.: Model-driven Software Engineering in Practice, 2nd edn. Morgan & Claypool Publishers, San Rafael (2017)Google Scholar
  2. 2.
    Cabrera, O., Franch, X., Marco, J.: 3LConOnt: a three-level ontology for context modelling in context-aware computing. Softw. Syst. Model. 18(2), 1345–1378 (2017). Scholar
  3. 3.
    Dąbrowski, J., Letier, E., Perini, A., Susi, A.: Finding and analyzing app reviews related to specific features: a research preview. In: Knauss, E., Goedicke, M. (eds.) REFSQ 2019. LNCS, vol. 11412, pp. 183–189. Springer, Cham (2019). Scholar
  4. 4.
    Ebert, C., Heidrich, J., Martinez-Fernandez, S., Trendowicz, A.: Data science: technologies for better software. IEEE Softw. 36(6), 66–72 (2019)CrossRefGoogle Scholar
  5. 5.
    Guzmán, L., Oriol, M., Rodríguez, P., Franch, X., Jedlitschka, A., Oivo, M.: How can quality awareness support rapid software development? – a research preview. In: Grünbacher, P., Perini, A. (eds.) REFSQ 2017. LNCS, vol. 10153, pp. 167–173. Springer, Cham (2017). Scholar
  6. 6.
    Jarke, M., Loucopoulos, P., Lyytinen, K., Mylopoulos, J., Robinson, W.: The brave new world of design requirements. Inf. Syst. 36(7), 992–1008 (2011)CrossRefGoogle Scholar
  7. 7.
    Johanssen, J.O., Kleebaum, A., Bruegge, B., Paech, B.: How do practitioners capture and utilize user feedback during continuous software engineering? In: Proceedings of RE (2019)Google Scholar
  8. 8.
    Lindgren, E., Münch, J.: Raising the odds of success: the current state of experimentation in product development. Inf. Softw. Technol. 77, 80–91 (2016)CrossRefGoogle Scholar
  9. 9.
    Maalej, W., Nayebi, M., Johann, T., Ruhe, G.: Toward data-driven requirements engineering. IEEE Softw. 33(1), 48–54 (2015)CrossRefGoogle Scholar
  10. 10.
    Maalej, W., Nayebi, M., Ruhe, G.: Data-driven requirements engineering: an update. In: Proceedings of ICSE/SEIP, pp. 289–290. IEEE (2019)Google Scholar
  11. 11.
    Martínez-Fernández, S., et al.: Continuously assessing and improving software quality with software analytics tools: a case study. IEEE Access 7, 68219–68239 (2019)CrossRefGoogle Scholar
  12. 12.
    Oriol, M., et al.: FAME: supporting continuous requirements elicitation by combining user feedback and monitoring. In: Proceedings of RE, pp. 217–227. IEEE (2018)Google Scholar
  13. 13.
    SonarQube: Accessed 24 Jan 2020
  14. 14.
    Vierhauser, M., Cleland-Huang, J., Burge, J., Grünbacher, P.: The interplay of design and runtime traceability for non-functional requirements. In: Proceedings of the 10th International Workshop on Software and Systems Traceability, pp. 3–10. IEEE (2019)Google Scholar
  15. 15.
    Villela, K., Groen, E.C., Doerr, J.: Ubiquitous requirements engineering: a paradigm shift that affects everyone. IEEE Softw. 36(2), 8–12 (2019)CrossRefGoogle Scholar
  16. 16.
    Wüest, D., Fotrousi, F., Fricker, S.: Combining monitoring and autonomous feedback requests to elicit actionable knowledge of system use. In: Knauss, E., Goedicke, M. (eds.) REFSQ 2019. LNCS, vol. 11412, pp. 209–225. Springer, Cham (2019). Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Universitat Politècnica de CatalunyaBarcelonaSpain
  2. 2.University of Applied Sciences and Arts Northwestern Switzerland FHNWWindischSwitzerland
  3. 3.Johannes Kepler University Linz & CDL-MINTLinzAustria

Personalised recommendations