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Controlled experimentation in continuous experimentation: Knowledge and challenges

Authors: Florian Auer; Rasmus Ros; Lukas Kaltenbrunner; Per Runeson; Michael Felderer;

Controlled experimentation in continuous experimentation: Knowledge and challenges

Abstract

Context: Continuous experimentation and A/B testing is an established industry practice that has been researched for more than 10 years. Our aim is to synthesize the conducted research. Objective: We wanted to find the core constituents of a framework for continuous experimentation and the solutions that are applied within the field. Finally, we were interested in the challenges and benefits reported of continuous experimentation. Method: We applied forward snowballing on a known set of papers and identified a total of 128 relevant papers. Based on this set of papers we performed two qualitative narrative syntheses and a thematic synthesis to answer the research questions. Results: The framework constituents for continuous experimentation include experimentation processes as well as supportive technical and organizational infrastructure. The solutions found in the literature were synthesized to nine themes, e.g. experiment design, automated experiments, or metric specification. Concerning the challenges of continuous experimentation, the analysis identified cultural, organizational, business, technical, statistical, ethical, and domain-specific challenges. Further, the study concludes that the benefits of experimentation are mostly implicit in the studies. Conclusions: The research on continuous experimentation has yielded a large body of knowledge on experimentation. The synthesis of published research presented within include recommended infrastructure and experimentation process models, guidelines to mitigate the identified challenges, and what problems the various published solutions solve.

Countries
Austria, Sweden
Keywords

FOS: Computer and information sciences, Domain specific, Programvaruteknik, PRODUCT, DEPLOYMENT, Process model, Computer Science - Software Engineering, Industry practices, Online controlled experiments, A, Information systems, Continuous experimentation, Controlled experimentation, OPTIMIZATION, Software engineering, Systematic literature review, Software Engineering, Organizational infrastructure, MODEL, Software Engineering (cs.SE), Body of knowledge, Research questions, CURRENT STATE, B testing, A/B testing, Experiment design

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
29
Top 10%
Top 10%
Top 10%
Green
hybrid