@inproceedings{soap2025,
    author    = {Hajdu, \'Akos and Lee, Roman and Weng, Gavin and Agrawal, Nilesh and Dubreil, J\'er\'emy},
    title     = {Compositional Static Callgraph Reachability Analysis for {W}hats{A}pp {A}ndroid App Health},
    year      = {2025},
    publisher = {ACM},
    booktitle = {Proceedings of the 14th ACM SIGPLAN International Workshop on the State Of the Art in Program Analysis},
    doi       = {10.1145/3735544.3735583},
    pages     = {15--21},

    url_pdf   = {https://hajduakos.github.io/publications/soap2025.pdf},
    url_link  = {https://dl.acm.org/doi/10.1145/3735544.3735583},
    url_slides = {https://hajduakos.github.io/publications/slides/soap2025.pdf},
    url_video = {https://www.youtube.com/live/JmvPLSBWxAw?si=4iqjUvXmpi2KqJde&t=2171},

    type      = {Workshop},

    abstract  = {We report on an industrial use case of static callgraph reachability analysis to improve WhatsApp Android app health. We collaborated with engineers dedicated to app health to annotate/specify source code. We leveraged the Infer static analyzer to prevent regressions during code changes and to periodically find pre-existing issues on the latest revision. Within three months, the analysis prevented almost a hundred regressions to be introduced and resulted in fixes for a handful of pre-existing issues, including examples with end-user measurable impact.},
    note      = {(Best presentation award)},
}

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