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However, compiler fuzzing remains challenging: Existing arts focus on black- and grey-box fuzzing, which generates test programs without sufficient understanding of internal compiler behaviors. As such, they often fail to construct test programs to exercise intricate optimizations. Meanwhile, traditional white-box techniques, such as symbolic execution, are computationally inapplicable to the giant codebase of compiler systems. Recent advances demonstrate that Large Language Models (LLMs) excel in code generation\/understanding tasks and even have achieved state-of-the-art performance in black-box fuzzing. Nonetheless, guiding LLMs with compiler source-code information remains a missing piece of research in compiler testing.<\/jats:p>\n                  <jats:p>\n                    To this end, we propose W\n                    <jats:sc>hite<\/jats:sc>\n                    F\n                    <jats:sc>ox<\/jats:sc>\n                    , the first white-box compiler fuzzer using LLMs with source-code information to test compiler optimization, with a spotlight on detecting deep logic bugs in the emerging deep learning (DL) compilers. W\n                    <jats:sc>hite<\/jats:sc>\n                    F\n                    <jats:sc>ox<\/jats:sc>\n                    adopts a multi-agent framework: (i) an LLM-based analysis agent examines the low-level optimization source code and produces requirements on the high-level test programs that can trigger the optimization; (ii) an LLM-based generation agent produces test programs based on the summarized requirements. Additionally, optimization-triggering tests are also used as feedback to further enhance the test generation prompt on the fly. Our evaluation on the three most popular DL compilers (\n                    <jats:italic toggle=\"yes\">i.e<\/jats:italic>\n                    ., PyTorch Inductor, TensorFlow-XLA, and TensorFlow Lite) shows that W\n                    <jats:sc>hite<\/jats:sc>\n                    F\n                    <jats:sc>ox<\/jats:sc>\n                    can generate high-quality test programs to exercise deep optimizations requiring intricate conditions, practicing up to 8 times more optimizations than state-of-the-art fuzzers. To date, W\n                    <jats:sc>hite<\/jats:sc>\n                    F\n                    <jats:sc>ox<\/jats:sc>\n                    has found in total 101 bugs for the compilers under test, with 92 confirmed as previously unknown and 70 already fixed. Notably, W\n                    <jats:sc>hite<\/jats:sc>\n                    F\n                    <jats:sc>ox<\/jats:sc>\n                    has been recently acknowledged by the PyTorch team, and is in the process of being incorporated into its development workflow. Finally, beyond DL compilers, W\n                    <jats:sc>hite<\/jats:sc>\n                    F\n                    <jats:sc>ox<\/jats:sc>\n                    can also be adapted for compilers in different domains, such as LLVM, where WHiteFox has already found multiple bugs.\n                  <\/jats:p>","DOI":"10.1145\/3689736","type":"journal-article","created":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T03:23:04Z","timestamp":1728357784000},"page":"709-735","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":52,"title":["WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models"],"prefix":"10.1145","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7976-5086","authenticated-orcid":false,"given":"Chenyuan","family":"Yang","sequence":"first","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Champaign, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4628-4219","authenticated-orcid":false,"given":"Yinlin","family":"Deng","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Champaign, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5261-6147","authenticated-orcid":false,"given":"Runyu","family":"Lu","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8588-4356","authenticated-orcid":false,"given":"Jiayi","family":"Yao","sequence":"additional","affiliation":[{"name":"Chinese University of Hong Kong, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7122-8625","authenticated-orcid":false,"given":"Jiawei","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Champaign, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0668-8526","authenticated-orcid":false,"given":"Reyhaneh","family":"Jabbarvand","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Champaign, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5175-2702","authenticated-orcid":false,"given":"Lingming","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Champaign, USA"}]}],"member":"320","published-online":{"date-parts":[[2024,10,8]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"2021. 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