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Differentiating Feature Realization in Software Product Development

  • Aleksander Fabijan
  • Helena Holmström Olsson
  • Jan Bosch
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10611)

Abstract

Context: Software is no longer only supporting mechanical and electrical products. Today, it is becoming the main competitive advantage and an enabler of innovation. Not all software, however, has an equal impact on customers. Companies still struggle to differentiate between the features that are regularly used, there to be for sale, differentiating and that add value to customers, or which are regarded commodity. Goal: The aim of this paper is to (1) identify the different types of software features that we can find in software products today, and (2) recommend how to prioritize the development activities for each of them. Method: In this paper, we conduct a case study with five large-scale software intensive companies. Results: Our main result is a model in which we differentiate between four fundamentally different types of features (e.g. ‘Checkbox’, ‘Flow’, ‘Duty’ and ‘Wow’). Conclusions: Our model helps companies in (1) differentiating between the feature types, and (2) selecting an optimal methodology for their development (e.g. ‘Output-Driven’ vs. ‘Outcome-Driven’).

Keywords

Data Feedback Outcome-driven development Data-driven development Goal-oriented development 

References

  1. 1.
    Fagerholm, F., Guinea, A.S., Mäenpää, H., Münch, J.: The RIGHT model for continuous experimentation. J. Syst. Softw. 0, 1–14 (2015)Google Scholar
  2. 2.
    Denne, M., Cleland-Huang, J.: The incremental funding method: data-driven software development. IEEE Softw. 21, 39–47 (2004)CrossRefGoogle Scholar
  3. 3.
    Boehm, B.: Value-based software engineering: reinventing. SIGSOFT Softw. Eng. Notes 28, 3 (2003)CrossRefGoogle Scholar
  4. 4.
    Khurum, M., Gorschek, T., Wilson, M.: The software value map - an exhaustive collection of value aspects for the development of software intensive products. J. Softw. Evol. Process. 25, 711–741 (2013)CrossRefGoogle Scholar
  5. 5.
    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, Cham (2015). doi: 10.1007/978-3-319-18612-2_10CrossRefGoogle Scholar
  6. 6.
    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, Cham (2015). doi: 10.1007/978-3-319-19593-3_13CrossRefGoogle Scholar
  7. 7.
    Fabijan, A., Olsson, H.H., Bosch, J.: Commodity eats innovation for breakfast: a model for differentiating feature realization. In: Abrahamsson, P., Jedlitschka, A., Nguyen Duc, A., Felderer, M., Amasaki, S., Mikkonen, T. (eds.) PROFES 2016. LNCS, vol. 10027, pp. 517–525. Springer, Cham (2016). doi: 10.1007/978-3-319-49094-6_37CrossRefGoogle Scholar
  8. 8.
    Martin, R.C.: Agile Software Development, Principles, Patterns, and Practices (2002)Google Scholar
  9. 9.
    Olsson, H.H., Alahyari, H., Bosch, J.: Climbing the “Stairway to heaven” - a multiple-case study exploring barriers in the transition from agile development towards continuous deployment of software. In: Proceedings of 38th EUROMICRO Conference on Software Engineering and Advanced Applications, SEAA 2012, pp. 392–399 (2012)Google Scholar
  10. 10.
    Mujtaba, S., Feldt, R., Petersen, K.: Waste and lead time reduction in a software product customization process with value stream maps. In: Proceedings of the Australian Software Engineering Conference, ASWEC, pp. 139–148 (2010)Google Scholar
  11. 11.
    Sedano, T., Ralph, P., Sedano, T.: Software development waste. In: Proceedings of the 39th International Conference on Software Engineering - ICSE 2017, pp. 130–140. IEEE Press, Buenos Aires (2017)Google Scholar
  12. 12.
    Goldratt, E.M., Cox, J.: The Goal: A Process of Ongoing Improvement. North River Press, Great Barrington (2004)Google Scholar
  13. 13.
    Rodríguez, P., Haghighatkhah, A., Lwakatare, L.E., Teppola, S., Suomalainen, T., Eskeli, J., Karvonen, T., Kuvaja, P., Verner, J.M., Oivo, M.: Continuous deployment of software intensive products and services: a systematic mapping study. J. Syst. Softw. 123, 263–291 (2015)CrossRefGoogle Scholar
  14. 14.
    Ries, E.: The Lean Startup: How Today’s Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses. Crown Business, New York (2011)Google Scholar
  15. 15.
    Fabijan, A.: Developing the right features: the role and impact of customer and product data in software product development (2016). https://dspace.mah.se/handle/2043/21268
  16. 16.
    Fabijan, A., Olsson, H.H., Bosch, J.: Customer feedback and data collection techniques in software R&D: a literature review. In: Fernandes, J., Machado, R., Wnuk, K. (eds.) ICSOB 2015. LNBIP, vol. 210, pp. 139–153. Springer, Cham (2015). doi: 10.1007/978-3-319-19593-3_12CrossRefGoogle Scholar
  17. 17.
    Williams, L., Cockburn, A.: Introduction: Agile Software Development: Its About Feedback and Change (2003) Google Scholar
  18. 18.
    Bosch-Sijtsema, P., Bosch, J.: User involvement throughout the innovation process in high-tech industries. J. Prod. Innov. Manag. 32, 1–36 (2014)Google Scholar
  19. 19.
    Kohavi, R., Deng, A., Frasca, B., Walker, T., Xu, Y., Pohlmann, N.: Online controlled experiments at large scale. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1168–1176 (2013)Google Scholar
  20. 20.
    Lindgren, E., Münch, J.: Raising the odds of success: the current state of experimentation in product development. Inf. Softw. Technol. 77, 80–91 (2015)CrossRefGoogle Scholar
  21. 21.
    Cao, L., Ramesh, B.: Agile requirements engineering practices: an empirical study. IEEE Softw. 25, 60–67 (2008)CrossRefGoogle Scholar
  22. 22.
    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: Proceedings of 40th Euromicro Conference Series on Software Engineering and Advanced Applications, SEAA 2014, pp. 9–16. IEEE (2014)Google Scholar
  23. 23.
    Manzi, J.: Uncontrolled: The Surprising Payoff of Trial-and-Error for Business, Politics, and Society. Basic Books, New York (2012)Google Scholar
  24. 24.
    The Standish Group: The Standish Group Report. Chaos, vol. 49, pp. 1–8 (1995)Google Scholar
  25. 25.
    Castellion, G.: Do it wrong quickly: how the web changes the old marketing rules by Mike Moran. J. Prod. Innov. Manag. 25, 633–635 (2008)CrossRefGoogle Scholar
  26. 26.
    Fabijan, A., Dmitriev, P., Olsson, H.H., Bosch, J.: The evolution of continuous experimentation in software product development: from data to a data-driven organization at scale. In: 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE), pp. 770–780. IEEE, Buenos Aires (2017)Google Scholar
  27. 27.
    Fabijan, A., Dmitriev, P., Olsson, H.H., Bosch, J.: The benefits of controlled experimentation at scale. In: 43rd Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Vienna, Austria. 30 August–1 September 2017. IEEE, Vienna (2017)Google Scholar
  28. 28.
    Davenport, T.H.: How to design smart business experiments (2009). https://hbr.org/2009/02/how-to-design-smart-business-experiments
  29. 29.
    Blank, S.: Why the lean start up changes everything. Harv. Bus. Rev. 91, 64 (2013)Google Scholar
  30. 30.
    Kohavi, R., Longbotham, R.: Online controlled experiments and A/B tests. In: Encyclopedia of Machine Learning and Data Mining, pp. 1–11 (2015)Google Scholar
  31. 31.
    Siroker, D., Koomen, P.: A/B testing - the most powerful way to turn clicks into customers (2012) Google Scholar
  32. 32.
    Kohavi, R., Longbotham, R., Sommerfield, D., Henne, R.M.: Controlled experiments on the web: survey and practical guide. Data Min. Knowl. Discov. 18, 140–181 (2009)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Tang, D., Agarwal, A., O’Brien, D., Meyer, M.: Overlapping experiment infrastructure. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2010, p. 17. ACM Press, New York (2010)Google Scholar
  34. 34.
    Kano, N., Seraku, N., Takahashi, F., Tsuji, S.: Attractive quality and must-be quality. J. Jpn. Soc. Qual. Control. 14, 39–48 (1984)Google Scholar
  35. 35.
    Bosch, J.: Achieving simplicity with the three-layer product model. Computer (Long Beach Calif.) 46, 34–39 (2013)Google Scholar
  36. 36.
    Moore, G.A.: Dealing with Darwin: How Great Companies Innovate at Every Phase of their Evolution. Penguin, New York (2005)Google Scholar
  37. 37.
    Runeson, P., Höst, M.: Guidelines for conducting and reporting case study research in software engineering. Empirical Softw. Eng. 14, 131–164 (2008)CrossRefGoogle Scholar
  38. 38.
    Mayring, P.: Qualitative content analysis - research instrument or mode of interpretation. In: The Role of the Researcher in Qualitative Psychology, pp. 139–148 (2002)Google Scholar
  39. 39.
    Augustine, S., Payne, B., Sencindiver, F., Woodcock, S.: Agile project management: steering from the edges. Commun. ACM 48, 85–89 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

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 TechnologyGothenburgSweden

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