Early Value Argumentation and Prediction: An Iterative Approach to Quantifying Feature Value

  • Aleksander Fabijan
  • Helena Holmström Olsson
  • Jan Bosch
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9459)


Companies are continuously improving their practices and ways of working in order to fulfill always-changing market requirements. As an example of building a better understanding of their customers, organizations are collecting user feedback and trying to direct their R&D efforts by e.g. continuing to develop features that deliver value to the customer. We (1) develop an actionable technique that practitioners in organizations can use to validate feature value early in the development cycle, (2) validate if and when the expected value reflects on the customers, (3) know when to stop developing it, and (4) identity unexpected business value early during development and redirect R&D effort to capture this value. The technique has been validated in three experiments in two cases companies. Our findings show that predicting value for features under development helps product management in large organizations to correctly re-prioritize R&D investments.


Continuous experimentation EVAP QCD Data-driven development Customer-driven development 


  1. 1.
    Cockburn, A., Williams, L.: Agile software development: it’s about feedback and change. Computer 36, 0039–43 (2003)Google Scholar
  2. 2.
    Dzamashvili Fogelström, N., Gorschek, T., Svahnberg, M., et al.: The impact of agile principles on market-driven software product development. J. Softw. Maint. Evol. Res. Pract. 22, 53–80 (2010)CrossRefGoogle Scholar
  3. 3.
    Olsson, H.H., Bosch, J.: From opinions to data-driven software R&D: a multi-case study on how to close the ‘open loop’ problem. In: 2014 40th EUROMICRO Conference on Software Engineering and Advanced Applications (SEAA), pp. 9–16 (2014)Google Scholar
  4. 4.
    Olsson, H.H., Bosch, J.: Towards continuous customer validation: a conceptual model for combining qualitative customer feedback with quantitative customer observation. In: Fernandes, J.M., Machado, R.J., Wnuk, K. (eds.) ICSOB 2015. LNBIP, vol. 210, pp. 154–166. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  5. 5.
    Von Hippel, E.: Lead users: a source of novel product concepts. Manage. Sci. 32, 791–805 (1986)Google Scholar
  6. 6.
    Bosch, J., Eklund, U.: Eternal embedded software: towards innovation experiment systems. In: Margaria, T., Steffen, B. (eds.) ISoLA 2012, Part I. LNCS, vol. 7609, pp. 19–31. Springer, Heidelberg (2012)Google Scholar
  7. 7.
  8. 8.
    Davenport, T. H.: How to design smart business experiments. In: Strategic Direction, Emerald Group Publishing Limited, Bradford (2009)Google Scholar
  9. 9.
    Fabijan, A., Olsson, H., Bosch, J.: Customer feedback and data collection techniques in software R&D: a literature review. In: Fernandes, J.M., Machado, R.J., Wnuk, K. (eds.) ICSOB 2015. LNBIP, vol. 210, pp. 139–153. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  10. 10.
    Bosch, J.: Building products as innovations experiment systems. In: Proceedings of 3rd International Conference on Software Business, Cambridge, 18–20 June 2012Google Scholar
  11. 11.
    Lindgren, E., Münch, J.: Software development as an experiment system: a qualitative survey on the state of the practice. In: Lassenius, C., Dingsøyr, T., Paasivaara, M. (eds.) XP 2015. LNBIP, vol. 212, pp. 117–128. Springer, Heidelberg (2015)Google Scholar
  12. 12.
    Fagerholm, F., Guinea, A.S., Mäenpää, H., et al.: Building blocks for continuous experimentation, pp. 26–35 (2014)Google Scholar
  13. 13.
    Ries, E.: The Lean Startup: How Today’s Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses. Random House LLC, New York (2011)Google Scholar
  14. 14.
    Walsham, G.: Interpretive case studies in IS research: nature and method. Eur. J. Inf. syst. 4, 74–81 (1995)CrossRefGoogle Scholar
  15. 15.
    Runeson, P., Höst, M.: Guidelines for conducting and reporting case study research in software engineering. Empirical Softw. Eng. 14, 131–164 (2009)CrossRefGoogle Scholar
  16. 16.
    Mayring, P.: Qualitative content analysis–research instrument or mode of interpretation. Role Researcher Qual. Psychol. 2, 139–148 (2002)Google Scholar
  17. 17.
    Johansson, E., Bergdahl, D., Bosch, J., Olsson, H.H.: Quantitative requirements prioritization from a pre-development perspective. In: Rout, T., O’Connor, R.V., Dorling, A. (eds.) SPICE 2015. CCIS, vol. 526, pp. 58–71. Springer, Heidelberg (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Aleksander Fabijan
    • 1
  • Helena Holmström Olsson
    • 1
  • Jan Bosch
    • 2
  1. 1.Faculty of Technology and SocietyMalmö UniversityMalmöSweden
  2. 2.Department of Computer Science and EngineeringChalmers University of TechnologyGöteborgSweden

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