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| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | We're doing it live: A multi-method empirical study on continuous experimentation |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Journal Title | Information and Software Technology |
| Publisher | Elsevier |
| Geographical Reach | international |
| ISSN | 0950-5849 |
| Volume | 99 |
| Page Range | 41 - 57 |
| Date | 2018 |
| Abstract Text | Context Continuous experimentation guides development activities based on data collected on a subset of online users on a new experimental version of the software. It includes practices such as canary releases, gradual rollouts, dark launches, or A/B testing. Objective Unfortunately, our knowledge of continuous experimentation is currently primarily based on well-known and outspoken industrial leaders. To assess the actual state of practice in continuous experimentation, we conducted a mixed-method empirical study. Method In our empirical study consisting of four steps, we interviewed 31 developers or release engineers, and performed a survey that attracted 187 complete responses. We analyzed the resulting data using statistical analysis and open coding. Results Our results lead to several conclusions: (1) from a software architecture perspective, continuous experimentation is especially enabled by architectures that foster independently deployable services, such as microservices-based architectures; (2) from a developer perspective, experiments require extensive monitoring and analytics to discover runtime problems, consequently leading to developer on call policies and influencing the role and skill sets required by developers; and (3) from a process perspective, many organizations conduct experiments based on intuition rather than clear guidelines and robust statistics. Conclusion Our findings show that more principled and structured approaches for release decision making are needed, striving for highly automated, systematic, and data- and hypothesis-driven deployment and experimentation. |
| Digital Object Identifier | 10.1016/j.infsof.2018.02.010 |
| Other Identification Number | merlin-id:17061 |
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