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The Benefits of Controlled Experimentation at Scale

Fabijan, Aleksander (author)
Malmö högskola,Institutionen för datavetenskap och medieteknik (DVMT),Malmö universitet,Malmö university
Dmitriev, Pavel (author)
Microsoft Analysis & Experimentation, Redmond, USA,Microsoft
Olsson, Helena Holmström (author)
Malmö högskola,Institutionen för datavetenskap och medieteknik (DVMT),Dep. of Computer Science, Chalmers University of Tech., Göteborg, Sweden,Malmö universitet,Malmö university
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Bosch, Jan, 1967 (author)
Chalmers Univ Tech, Dept Comp Sci, Gothenburg, Sweden,Chalmers tekniska högskola,Chalmers University of Technology
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 (creator_code:org_t)
ISBN 9781538621400
IEEE, 2017
2017
English.
In: 2017 43rd Euromicro Conference on Software Engineering and Advanced Applications (SEAA). - : IEEE. - 9781538621400 ; , s. 18-26
  • Conference paper (peer-reviewed)
Abstract Subject headings
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  • Online controlled experiments (for example A/B tests) are increasingly being performed to guide product development and accelerate innovation in online software product companies. The benefits of controlled experiments have been shown in many cases with incremental product improvement as the objective. In this paper, we demonstrate that the value of controlled experimentation at scale extends beyond this recognized scenario. Based on an exhaustive and collaborative case study in a large software-intensive company with highly developed experimentation culture, we inductively derive the benefits of controlled experimentation. The contribution of our paper is twofold. First, we present a comprehensive list of benefits and illustrate our findings with five case examples of controlled experiments conducted at Microsoft. Second, we provide guidance on how to achieve each of the benefits. With our work, we aim to provide practitioners in the online domain with knowledge on how to use controlled experimentation to maximize the benefits on the portfolio, product and team level.

Subject headings

NATURVETENSKAP  -- Data- och informationsvetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences (hsv//eng)

Keyword

controlled experimentation
leading metrics
lagging metrics
data-driven development

Publication and Content Type

ref (subject category)
kon (subject category)

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