Controlled experimentation in continuous experimentation: Knowledge and challenges

被引:21
作者
Auer, Florian [1 ]
Ros, Rasmus [2 ]
Kaltenbrunner, Lukas [1 ]
Runeson, Per [2 ]
Felderer, Michael [1 ,3 ]
机构
[1] Univ Innsbruck, Innsbruck, Austria
[2] Lund Univ, Lund, Sweden
[3] Blekinge Inst Technol, Blekinge, Sweden
基金
奥地利科学基金会;
关键词
Continuous experimentation; Online controlled experiments; A; B testing; Systematic literature review; CURRENT STATE; DEPLOYMENT; OPTIMIZATION; PRODUCT; MODEL;
D O I
10.1016/j.infsof.2021.106551
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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. Methods: 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. Conclusion: 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.
引用
收藏
页数:16
相关论文
共 160 条
[1]   A Nonparametric Sequential Test for Online Randomized Experiments [J].
Abhishek, Vineet ;
Mannor, Shie .
WWW'17 COMPANION: PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, 2017, :610-616
[2]   Beyond Data: From User Information to Business Value through Personalized Recommendations and Consumer Science [J].
Amatriain, Xavier .
PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), 2013, :2201-2207
[3]  
[Anonymous], 2018, P 13 INT C SOFTW ENG, DOI DOI 10.1145/3194133.3194152
[4]  
[Anonymous], 2012, Experimentation in Software Engineering
[5]  
[Anonymous], 2018 44 EUR C SOFTW
[6]  
[Anonymous], 2014, 2014 INT C DAT SOFTW, DOI DOI 10.1109/ICODSE.2014.7062683
[7]  
Appiktala N, 2017, IEEE INT CONF BIG DA, P1620, DOI 10.1109/BigData.2017.8258096
[8]  
Auer F., 2021, DATASET CONTROLLED E, DOI DOI 10.6084/M9.FIGSHARE.13712329
[9]   Current State of Research on Continuous Experimentation: A Systematic Mapping Study [J].
Auer, Florian ;
Felderer, Michael .
44TH EUROMICRO CONFERENCE ON SOFTWARE ENGINEERING AND ADVANCED APPLICATIONS (SEAA 2018), 2018, :335-344
[10]   Estimation Errors in Network A/B Testing due to Sample Variance and Model Misspecification [J].
Azevedo, Francisco Galuppo ;
Nogueira, Bruno Demattos ;
Murai, Fabricio ;
Silva, Ana Paula C. .
2018 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE (WI 2018), 2018, :540-545