Facebook’s Cyber–Cyber and Cyber–Physical Digital Twins

Ahlgren, John; Bojarczuk, Kinga; Drossopoulou, Sophia; Dvortsova, Inna; George, Johann; Gucevska, Natalija; Harman, Mark; Lomelí, María; Lucas, Simon M.; Meijer, Erik; Omohundro, Steve; Rojas, Ruben Gregorio Ricra; Sapora, Silvia; Zhou, Norm

doi:10.1145/3463274.3463275

Cited by 27 publications

(17 citation statements)

References 46 publications

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“…Traditional tests, on the other hand, involve executing a predetermined series of input steps. WES is run in-vivo but in a shadow copy of the system, hence as a separate "digital twin", which allows for testing without risk to real user data [2]. Both "digital twins" and WES have widespread industrial applications, not only in OSNs but also in robotics, manufacturing, healthcare, transport, and the energy sector, among others [19,32].…”

Section: Testing Of Social Networkmentioning

confidence: 99%

Socialz: Multi-Feature Social Fuzz Testing

Zanartu¹,

Wagner²

2023

Preprint

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Online social networks have become an integral aspect of our daily lives and play a crucial role in shaping our relationships with others. However, bugs and glitches, even minor ones, can cause anything from frustrating problems to serious data leaks that can have farreaching impacts on millions of users.To mitigate these risks, fuzz testing, a method of testing with randomised inputs, can provide increased confidence in the correct functioning of a social network. However, implementing traditional fuzz testing methods can be prohibitively difficult or impractical for programmers outside of the network's development team.To tackle this challenge, we present Socialz, a novel approach to social fuzz testing that (1) characterises real users of a social network, (2) diversifies their interaction using evolutionary computation across multiple, non-trivial features, and (3) collects performance data as these interactions are executed. With Socialz, we aim to provide anyone with the capability to perform comprehensive social testing, thereby improving the reliability and security of online social networks used around the world.

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Section: Testing Of Social Networkmentioning

confidence: 99%

Socialz: Multi-Feature Social Fuzz Testing

Zanartu¹,

Wagner²

2023

Preprint

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“…In this paper we present and evaluate Meta's automated testing tool, ALPACAS: Automatically-Learnt Production-ACcumulated Action Simulations, that infers oracles automatically, at scale, from production traffic, on Meta Platforms such as Facebook and Instagram. ALPACAS is part of the Metamorphic Interaction Automaton (MIA) [2], deployed on Meta's WW simulation platform [3]. The simulation framework, WW, The primary author is Shreshth Tuli who is a PhD student at Imperial College London.…”

Section: Introductionmentioning

confidence: 99%

“…WW simulations have highly realistic behavioural outcomes, due to the bot interactions being executed on the real platform infrastructure. More details on Meta's simulation-based approach to analysis, testing and optimisation can be found elsewhere [3], [4], [5], [6].…”

Section: Introductionmentioning

confidence: 99%

“…Although the present paper focuses exclusively on the applicability of ALPACAS to integrity testing, the underlying principles apply more widely to the inference of test signals from checks performed in production systems. Such feedback signals aid in making systems more robust, leveraging cybercyber digital-twins [3] for software improvement and closing the production-simulation loop.…”

Section: Introductionmentioning

confidence: 99%

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Simulation-Driven Automated End-to-End Test and Oracle Inference

Tuli¹,

Bojarczuk²,

Gucevska³

et al. 2023

Preprint

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This is the first work to report on inferential testing at scale in industry. Specifically, it reports the experience of automated testing of integrity systems at Meta. We built an internal tool called ALPACAS for automated inference of end-toend integrity tests. Integrity tests are designed to keep users safe online by checking that interventions take place when harmful behaviour occurs on a platform. ALPACAS infers not only the test input, but also the oracle, by observing production interventions to prevent harmful behaviour. This approach allows Meta to automate the process of generating integrity tests for its platforms, such as Facebook and Instagram, which consist of hundreds of millions of lines of production code. We outline the design and deployment of ALPACAS, and report results for its coverage, number of tests produced at each stage of the test inference process, and their pass rates. Specifically, we demonstrate that using ALPACAS significantly improves coverage from a manual test design for the particular aspect of integrity end-to-end testing it was applied to. Further, from a pool of 3 million data points, ALPACAS automatically yields 39 production-ready end-to-end integrity tests. We also report that the ALPACAS-inferred test suite enjoys exceptionally low flakiness for end-to-end testing with its average in-production pass rate of 99.84%.

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“…For certain types of systems, simulation also plays a key role in later stages of development, e.g., when one needs to exercise scenarios that are too risky or too expensive to run in real-world deployments. Notably, simulation is the technique of choice for the validation and verification of systems that have high levels of autonomy, e.g., autonomous vehicles [8,9,13], or massive connectivity, e.g., the Internet of things (IoT) [28,43].…”

Section: Introductionmentioning

confidence: 99%

A Domain-Specific Language for Simulation-Based Testing of IoT Edge-to-Cloud Solutions

Li¹,

Nejati²,

Sabetzadeh³

et al. 2022

Preprint

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The Internet of things (IoT) is increasingly prevalent in domains such as emergency response, smart cities and autonomous vehicles. Simulation plays a key role in the testing of IoT systems, noting that field testing of a complete IoT product may be infeasible or prohibitively expensive. In this paper, we propose a domain-specific language (DSL) for generating edge-to-cloud simulators. An edgeto-cloud simulator executes the functionality of a large array of edge devices that communicate with cloud applications. Our DSL, named IoTECS, is the result of a collaborative project with an IoT analytics company, Cheetah Networks. The industrial use case that motivates IoTECS is ensuring the scalability of cloud applications by putting them under extreme loads from IoT devices connected to the edge. We implement IoTECS using Xtext and empirically evaluate its usefulness. We further reflect on the lessons learned.

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Facebook’s Cyber–Cyber and Cyber–Physical Digital Twins

Cited by 27 publications

References 46 publications

Socialz: Multi-Feature Social Fuzz Testing

Socialz: Multi-Feature Social Fuzz Testing

Simulation-Driven Automated End-to-End Test and Oracle Inference

A Domain-Specific Language for Simulation-Based Testing of IoT Edge-to-Cloud Solutions

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