We introduce Fausta, an algorithmic traffic gener-ation platform that enables analysis and testing at scale. Fausta has been deployed at Meta to analyze and test the WhatsApp plat-form infrastructure since September 2020, enabling WhatsApp developers to deploy reliable code changes to a code base of millions of lines of code, supporting over 2 billion users who rely on WhatsApp for their daily communications. Fausta covers expected and unexpected program behaviors in a privacy-safe controlled environment to support multiple use cases such as reliability testing, privacy analysis and performance regression detection. It currently supports three different algorithmic input generation strategies, each of which construct realistic backend server traffic that closely simulates production data, without replaying any real user data. Fausta has been deployed and closely integrated into the WhatsApp continuous integration process, catching bugs in development before they hit production. We report on the development and deployment of Fausta's reliability use case between September 2020 and August 2021. During this period it has found 1,876 unique reliability issues, with a fix rate of 74%, indicating a high degree of true positive fault revelation. We also report on the distribution of fault types revealed by Fausta, and the correlation between coverage and faults found. Overall, we do find evidence that higher coverage is correlated with fault revelation.

People do most of their activities, business, commerce, entertainment and socializing indoors. As all of these are increasingly aided by online services and indoor spaces are becoming bigger and more complex, there is a growing need for cost-effective indoor localization, mapping, navigation and information services. In this paper, we present a complete Indoor Information Service, coined Anyplace, which has an open, modular, extensible and scalable architecture, making it ideal for a wide range of applications. Our service features three highly desirable properties, namely crowd sourcing, scalability and accuracy. Anyplace implements a set of crowd sourcing-supportive mechanisms to handle the enormous amount of crowd-sensed data, filter incorrect user contributions and exploit Wi-Fi data from heterogeneous mobile devices. Moreover, it uses a big-data architecture for efficient storage and retrieval of localization and mapping data. Finally, our service relies on the abundance of sensory data on smartphones (e.g., Wi-Fi signal strength and inertial measurements) to deliver reliable indoor geolocation information that received several international awards.