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How Do Software Startups Approach Experimentation? Empirical Results from a Qualitative Interview Study

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Product-Focused Software Process Improvement (PROFES 2017)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10611))

Abstract

Software startups often make assumptions about the problems and customers they are addressing as well as the market and the solutions they are developing. Testing the right assumptions early is a means to mitigate risks. Approaches such as Lean Startup foster this kind of testing by applying experimentation as part of a constant build-measure-learn feedback loop. The existing research on how software startups approach experimentation is very limited. In this study, we focus on understanding how software startups approach experimentation and identify challenges and advantages with respect to conducting experiments. To achieve this, we conducted a qualitative interview study. The initial results show that startups often spent a disproportionate amount of time focusing on creating solutions without testing critical assumptions. Main reasons are the lack of awareness, that these assumptions can be tested early and a lack of knowledge and support on how to identify, prioritize and test these assumptions. However, startups understand the need for testing risky assumptions and are open to conducting experiments.

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Acknowledgements

We wish to thank all participants for their time and contributions.

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Correspondence to Matthias Gutbrod .

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Gutbrod, M., Münch, J., Tichy, M. (2017). How Do Software Startups Approach Experimentation? Empirical Results from a Qualitative Interview Study. In: Felderer, M., Méndez Fernández, D., Turhan, B., Kalinowski, M., Sarro, F., Winkler, D. (eds) Product-Focused Software Process Improvement. PROFES 2017. Lecture Notes in Computer Science(), vol 10611. Springer, Cham. https://doi.org/10.1007/978-3-319-69926-4_21

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  • DOI: https://doi.org/10.1007/978-3-319-69926-4_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69925-7

  • Online ISBN: 978-3-319-69926-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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