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From Efficiency to Effectiveness: Delivering Business Value Through Software

  • Jan BoschEmail author
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 370)

Abstract

Connected products and DevOps allow for a fundamentally different way of working in R&D. Rather than focusing on efficiency of teams, often expressed in terms of flow and number of features per sprint, we are now able to focus on the effectiveness of R&D as expressed in the amount of value created per unit of R&D. We have developed several solutions, such as HYPEX, HoliDev and hierarchical value models, but companies still experience challenges. In this paper, we provide an overview of the trends driving the transition to focusing on effectiveness, discuss the challenges that companies experience as well as the requirements for a successful transformation.

Keywords

Efficiency Effectiveness Data-driven development AI-driven development 

Notes

Acknowledgment

The work reported in this article is the result of collaborations with many researchers in the context of Software Center, a collaboration between, at the time of writing, thirteen companies and five universities.

References

  1. 1.
    Arpteg, A., Brinne, B., Crnkovic-Friis, L., Bosch, J.: Software engineering challenges of deep learning. In: 2018 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), pp. 50–59. IEEE August (2018)Google Scholar
  2. 2.
    Bosch, J., Olsson, H.H.: Toward evidence-based organizations: lessons from embedded systems, online games, and the Internet of Things. IEEE Softw. 34(5), 60–66 (2017)CrossRefGoogle Scholar
  3. 3.
    Bosch, J.: Speed, Data, and Ecosystems: Excelling in a Software-Driven World. CRC Press, Boca Raton (2017)CrossRefGoogle Scholar
  4. 4.
    Bosch, J., Olsson, H.H., Crnkovic, I.: It takes three to tango: Requirement, outcome/data, and AI driven development. In: SiBW (2018)Google Scholar
  5. 5.
    Bosch, J.: Towards a digital business operating system. In: Proceedings of RCIS 2019, to appear (2019)Google Scholar
  6. 6.
    Fabijan, A., et al.: Experimentation growth: evolving trustworthy A/B testing capabilities in online software companies. J. Softw.: Evol. Process 30(12), e2113 (2018)Google Scholar
  7. 7.
    Lwakatare, L.E., Raj, A., Bosch, J., Olsson, H.H., Crnkovic, I.: A taxonomy of software engineering challenges for machine learning systems: an empirical investigation. In: Kruchten, P., Fraser, S., Coallier, F. (eds.) XP 2019. LNBIP, vol. 355, pp. 227–243. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-19034-7_14CrossRefGoogle Scholar
  8. 8.
    Mattos I.D., Bosch, J., Olsson, H.H.: Challenges and strategies for undertaking continuous experimentation to embedded systems: industry and research perspectives. In: XP 2018: Agile Processes in Software Engineering and Extreme Programming, pp. 277–292 (2018)Google Scholar
  9. 9.
    Mattos, D.I., Bosch, J., Olsson, H.H.: Multi-armed bandits in the wild: pitfalls and strategies in online experiments. Inf. Softw. Technol. 113, 68–81 (2019)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringChalmers University of TechnologyGothenburgSweden

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