@inproceedings{erlang2022,
    author     = {{\'A}kos Hajdu and Matteo Marescotti and Thibault Suzanne and Ke Mao and Radu Grigore and Per Gustafsson and Dino Distefano},
    title      = {{InfERL}: Scalable and Extensible {E}rlang Static Analysis},
    year       = {2022},
    booktitle  = {Proceedings of the 21st ACM SIGPLAN International Workshop on Erlang},
    pages      = {33--39},
    publisher  = {ACM},
    doi        = {10.1145/3546186.3549929},

    type       = {Workshop},

    url_pdf    = {https://hajduakos.github.io/publications/erlang2022.pdf},
    url_slides = {https://hajduakos.github.io/publications/slides/erlang2022.pdf},
    url_link   = {https://dl.acm.org/doi/abs/10.1145/3546186.3549929},
    url_video  = {https://www.youtube.com/watch?v=ZDZtauSxyaE},
    abstract   = {In this paper we introduce InfERL, an open source, scalable, and extensible static analyzer for Erlang, based on Meta's Infer tool. InfERL has been developed at WhatsApp and it is deployed to regularly scan WhatsApp server's Erlang codebase, detecting reliability issues and checking user-defined properties. The paper describes the Erlang specific technical challenges we had to address and our design choices. We also report on our experience in running InfERL on Erlang code at scale, supporting the messaging app used everyday by over 2 billion people.},
}

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