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Softw. Eng. Methodol."],"published-print":{"date-parts":[[2024,1,31]]},"abstract":"<jats:p>\n            Control flow recovery is critical to promise the software quality, especially for large-scale software in production environment. However, the efficiency of most current control flow recovery techniques is compromised due to their runtime overheads along with deployment and development costs. To tackle this problem, we propose a novel solution,\n            <jats:italic>Adonis<\/jats:italic>\n            , which harnesses\n            <jats:bold>Operating System (OS)<\/jats:bold>\n            -level traces, such as dynamic library calls and system call traces, to efficiently and safely recover control flows in practice.\n            <jats:italic>Adonis<\/jats:italic>\n            operates in two steps: It first identifies the call-sites of trace entries, and then it executes a pairwise symbolic execution to recover valid execution paths. This technique has several advantages. First,\n            <jats:italic>Adonis<\/jats:italic>\n            does not require the insertion of any probes into existing applications, thereby minimizing\n            <jats:italic>runtime cost<\/jats:italic>\n            . Second, given that OS-level traces are hardware-independent,\n            <jats:italic>Adonis<\/jats:italic>\n            can be implemented across various hardware configurations without the need for hardware-specific engineering efforts, thus reducing\n            <jats:italic>deployment cost<\/jats:italic>\n            . Third, as\n            <jats:italic>Adonis<\/jats:italic>\n            is fully automated and does not depend on manually created logs, it circumvents additional\n            <jats:italic>development cost<\/jats:italic>\n            . We conducted an evaluation of\n            <jats:italic>Adonis<\/jats:italic>\n            on representative desktop applications and real-world IoT applications.\n            <jats:italic>Adonis<\/jats:italic>\n            can faithfully recover the control flow with 86.8% recall and 81.7% precision. Compared to the state-of-the-art log-based approach,\n            <jats:italic>Adonis<\/jats:italic>\n            can not only cover all the execution paths recovered but also recover 74.9% of statements that cannot be covered. In addition, the runtime cost of\n            <jats:italic>Adonis<\/jats:italic>\n            is 18.3\u00d7 lower than the instrument-based approach; the analysis time and storage cost (indicative of the deployment cost) of\n            <jats:italic>Adonis<\/jats:italic>\n            is 50\u00d7 smaller and 443\u00d7 smaller than the hardware-based approach, respectively. To facilitate future replication and extension of this work, we have made the code and data publicly available.\n          <\/jats:p>","DOI":"10.1145\/3607187","type":"journal-article","created":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T13:26:34Z","timestamp":1688477194000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["<i>Adonis<\/i>\n            : Practical and Efficient Control Flow Recovery through OS-level Traces"],"prefix":"10.1145","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7908-8484","authenticated-orcid":false,"given":"Xuanzhe","family":"Liu","sequence":"first","affiliation":[{"name":"Peking University, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6991-7195","authenticated-orcid":false,"given":"Chengxu","family":"Yang","sequence":"additional","affiliation":[{"name":"Peking University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7558-9137","authenticated-orcid":false,"given":"Ding","family":"Li","sequence":"additional","affiliation":[{"name":"Peking University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0729-7391","authenticated-orcid":false,"given":"Yuhan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Peking University, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6530-5935","authenticated-orcid":false,"given":"Shaofei","family":"Li","sequence":"additional","affiliation":[{"name":"Peking University, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5755-3681","authenticated-orcid":false,"given":"Jiali","family":"Chen","sequence":"additional","affiliation":[{"name":"Peking University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4765-1893","authenticated-orcid":false,"given":"Zhenpeng","family":"Chen","sequence":"additional","affiliation":[{"name":"University College London, United Kingdom"}]}],"member":"320","published-online":{"date-parts":[[2023,11,24]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Agile-IoT. 2022. 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