Engineering for a Science-Centric Experimentation Platform

Netflix is an internet entertainment service that routinely employs experimentation to guide strategy around product innovations. As Netflix grew, it had the opportunity to explore increasingly specialized improvements to its service, which generated demand for deeper analyses supported by richer metrics and powered by more diverse statistical methodologies. To facilitate this, and more fully harness the skill sets of both engineering and data science, Netflix engineers created a science-centric experimentation platform that leverages the expertise of data scientists from a wide range of backgrounds by allowing them to make direct code contributions in the languages used by scientists (Python and R). Moreover, the same code that runs in production is able to be run locally, making it straightforward to explore and graduate both metrics and causal inference methodologies directly into production services. In this paper, we utilize a case-study research method to provide two main contributions. Firstly, we report on the architecture of this platform, with a special emphasis on its novel aspects: how it supports science-centric end-to-end workflows without compromising engineering requirements. Secondly, we describe its approach to causal inference, which leverages the potential outcomes conceptual framework to provide a unified abstraction layer for arbitrary statistical models and methodologies.Comment: 10 page

Engineering for a science-centric experimentation platform

Netflix is an internet entertainment service that routinely employs experimentation to guide strategy around product innovations. As Netflix grew, it had the opportunity to explore increasingly specialized improvements to its service, which generated demand for deeper analyses supported by richer metrics and powered by more diverse statistical methodologies. To facilitate this, and more fully harness the skill sets of both engineering and data science, Netflix engineers created a science-centric experimentation platform that leverages the expertise of scientists from a wide range of backgrounds working on data science tasks by allowing them to make direct code contributions in the languages used by them (Python and R). Moreover, the same code that runs in production is able to be run locally, making it straightforward to explore and graduate both metrics and causal inference methodologies directly into production services. In this paper, we provide two main contributions. Firstly, we report on the architecture of this platform, with a special emphasis on its novel aspects: how it supports science-centric end-to-end workflows without compromising engineering requirements. Secondly, we describe its approach to causal inference, which leverages the potential outcomes conceptual framework to provide a unified abstarction layer for arbitrary statistical models and methodologies

On Experimentation in Software-Intensive Systems

Context: Delivering software that has value to customers is a primary concern of every software company. Prevalent in web-facing companies, controlled experiments are used to validate and deliver value in incremental deployments. At the same that web-facing companies are aiming to automate and reduce the cost of each experiment iteration, embedded systems companies are starting to adopt experimentation practices and leverage their activities on the automation developments made in the online domain. Objective: This thesis has two main objectives. The first objective is to analyze how software companies can run and optimize their systems through automated experiments. This objective is investigated from the perspectives of the software architecture, the algorithms for the experiment execution and the experimentation process. The second objective is to analyze how non web-facing companies can adopt experimentation as part of their development process to validate and deliver value to their customers continuously. This objective is investigated from the perspectives of the software development process and focuses on the experimentation aspects that are distinct from web-facing companies. Method: To achieve these objectives, we conducted research in close collaboration with industry and used a combination of different empirical research methods: case studies, literature reviews, simulations, and empirical evaluations. Results: This thesis provides six main results. First, it proposes an architecture framework for automated experimentation that can be used with different types of experimental designs in both embedded systems and web-facing systems. Second, it proposes a new experimentation process to capture the details of a trustworthy experimentation process that can be used as the basis for an automated experimentation process. Third, it identifies the restrictions and pitfalls of different multi-armed bandit algorithms for automating experiments in industry. This thesis also proposes a set of guidelines to help practitioners select a technique that minimizes the occurrence of these pitfalls. Fourth, it proposes statistical models to analyze optimization algorithms that can be used in automated experimentation. Fifth, it identifies the key challenges faced by embedded systems companies when adopting controlled experimentation, and we propose a set of strategies to address these challenges. Sixth, it identifies experimentation techniques and proposes a new continuous experimentation model for mission-critical and business-to-business. Conclusion: The results presented in this thesis indicate that the trustworthiness in the experimentation process and the selection of algorithms still need to be addressed before automated experimentation can be used at scale in industry. The embedded systems industry faces challenges in adopting experimentation as part of its development process. In part, this is due to the low number of users and devices that can be used in experiments and the diversity of the required experimental designs for each new situation. This limitation increases both the complexity of the experimentation process and the number of techniques used to address this constraint

Towards Automated Experiments in Software Intensive Systems

Context: Delivering software that has value to customers is a primary concern of every software company. One of the techniques to continuously validate and deliver value in online software systems is the use of controlled experiments. The time cost of each experiment iteration, the increasing growth in the development organization to run experiments and the need for a more automated and systematic approach is leading companies to look for different techniques to automate the experimentation process. Objective: The overall objective of this thesis is to analyze how to automate different types of experiments and how companies can support and optimize their systems through automated experiments. This thesis explores the topic of automated online experiments from the perspectives of the software architecture, the algorithms for the experiment execution and the experimentation process, and focuses on two main application domains: the online and the embedded systems domain. Method: To achieve the objective, we conducted this research in close collaboration with industry using a combination of different empirical research methods: case studies, literature reviews, simulations and empirical evaluations. Results and conclusions: This thesis provides five main results. First, we propose an architecture framework for automated experimentation that can be used with different types of experimental designs in both embedded systems and web-facing systems. Second, we identify the key challenges faced by embedded systems companies when adopting controlled experimentation and we propose a set of strategies to address these challenges. Third, we develop a new algorithm for online experiments. Fourth, we identify restrictions and pitfalls of different algorithms for automating experiments in industry and we propose a set of guidelines to help practitioners select a technique that minimizes the occurrence of these pitfalls. Fifth, we propose a new experimentation process to capture the details of a trustworthy experimentation process that can be used as basis for an automated experimentation process. Future work: In future work, we plan to investigate how embedded systems can incorporate experiments in their development process without compromising existing real-time and safety requirements. We also plan to analyze the impact and costs of automating the different parts of the experimentation process

An activity and metric model for online controlled experiments

Accurate prioritization of efforts in product and services development is critical to the success of every company. Online controlled experiments, also known as A/B tests, enable software companies to establish causal relationships between changes in their systems and the movements in the metrics. By experimenting, product development can be directed towards identifying and delivering value. Previous research stresses the need for data-driven development and experimentation. However, the level of granularity in which existing models explain the experimentation process is neither sufficient, in terms of details, nor scalable, in terms of how to increase number and run different types of experiments, in an online setting. Based on a case study of multiple products running online controlled experiments at Microsoft, we provide an experimentation framework composed of two detailed experimentation models focused on two main aspects; the experimentation activities and the experimentation metrics. This work intends to provide guidelines to companies and practitioners on how to set and organize experimentation activities for running trustworthy online controlled experiments