{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T21:20:07Z","timestamp":1770240007182,"version":"3.49.0"},"reference-count":76,"publisher":"Association for Computing Machinery (ACM)","issue":"FSE","license":[{"start":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T00:00:00Z","timestamp":1720742400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Softw. Eng."],"published-print":{"date-parts":[[2024,7,12]]},"abstract":"<jats:p>\n                    Reverse engineers would acquire valuable insights from descriptive function names, which are absent in publicly released binaries. Recent advances in binary function name prediction using data-driven machine learning show promise. However, existing approaches encounter difficulties in capturing function semantics in diverse optimized binaries and fail to reserve the meaning of labels in function names. We propose E\n                    <jats:sc>pitome<\/jats:sc>\n                    , a framework that enhances function name prediction using votes-based name tokenization and multi-task learning, specifically tailored for different compilation optimization binaries. E\n                    <jats:sc>pitome<\/jats:sc>\n                    learns comprehensive function semantics by pre-trained assembly language model and graph neural network, incorporating function semantics similarity prediction task, to maximize the similarity of function semantics in the context of different compilation optimization levels. In addition, we present two data preprocessing methods to improve the comprehensibility of function names. We evaluate the performance of E\n                    <jats:sc>pitome<\/jats:sc>\n                    using\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\">\n                        <mml:mrow>\n                          <mml:mn>2<\/mml:mn>\n                          <mml:mo>,<\/mml:mo>\n                          <mml:mn>597<\/mml:mn>\n                          <mml:mo>,<\/mml:mo>\n                          <mml:mn>346<\/mml:mn>\n                        <\/mml:mrow>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    functions extracted from binaries compiled with 5 optimizations (O0-Os) for 4 architectures (x64, x86, ARM, and MIPS). E\n                    <jats:sc>pitome<\/jats:sc>\n                    outperforms the state-of-the-art function name prediction tool by up to\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\">\n                        <mml:mn>44.34<\/mml:mn>\n                        <mml:mo>%<\/mml:mo>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    ,\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\">\n                        <mml:mn>64.16<\/mml:mn>\n                        <mml:mo>%<\/mml:mo>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    , and\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\">\n                        <mml:mn>54.44<\/mml:mn>\n                        <mml:mo>%<\/mml:mo>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    in precision, recall, and F1 score, while also exhibiting superior generalizability.\n                  <\/jats:p>","DOI":"10.1145\/3660782","type":"journal-article","created":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T10:22:09Z","timestamp":1720779729000},"page":"1679-1702","source":"Crossref","is-referenced-by-count":4,"title":["Enhancing Function Name Prediction using Votes-Based Name Tokenization and Multi-task Learning"],"prefix":"10.1145","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-0906-1817","authenticated-orcid":false,"given":"Xiaoling","family":"Zhang","sequence":"first","affiliation":[{"name":"Institute of Information Engineering at Chinese Academy of Sciences, Beijing, China"},{"name":"University of Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8390-7518","authenticated-orcid":false,"given":"Zhengzi","family":"Xu","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4385-8261","authenticated-orcid":false,"given":"Shouguo","family":"Yang","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering at Chinese Academy of Sciences, Beijing, China"},{"name":"University of Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7071-2976","authenticated-orcid":false,"given":"Zhi","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering at Chinese Academy of Sciences, Beijing, China"},{"name":"University of Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6168-8003","authenticated-orcid":false,"given":"Zhiqiang","family":"Shi","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering at Chinese Academy of Sciences, Beijing, China"},{"name":"University of Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2745-7521","authenticated-orcid":false,"given":"Limin","family":"Sun","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering at Chinese Academy of Sciences, Beijing, China"},{"name":"University of Chinese Academy of Sciences, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2024,7,12]]},"reference":[{"key":"e_1_3_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3359591.3359735"},{"key":"e_1_3_1_3_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1808.01400"},{"key":"e_1_3_1_4_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1912.07946"},{"key":"e_1_3_1_5_1","doi-asserted-by":"publisher","unstructured":"Eran Avidan and Dror G. Feitelson. 2017. Effects of Variable Names on Comprehension: An Empirical Study. In 2017 IEEE\/ACM 25th International Conference on Program Comprehension (ICPC). 55\u201365. https:\/\/doi.org\/10.1109\/ICPC.2017.27 10.1109\/ICPC.2017.27","DOI":"10.1109\/ICPC.2017.27"},{"key":"e_1_3_1_6_1","volume-title":"Implementation patterns","author":"Beck Kent","year":"2007","unstructured":"Kent Beck. 2007. Implementation patterns. Pearson Education."},{"key":"e_1_3_1_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/S10579-010-9124-X"},{"key":"e_1_3_1_8_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl-a-00051"},{"key":"e_1_3_1_9_1","unstructured":"Buildroot. 2023. Buildroot: making embedded linux easy. Retrieved June 5 2023 from https:\/\/buildroot.org"},{"key":"e_1_3_1_10_1","doi-asserted-by":"publisher","unstructured":"M. Ceccato P. Tonella C. Basile B. Coppens B. De Sutter P. Falcarin and M. Torchiano. 2017. How Professional Hackers Understand Protected Code while Performing Attack Tasks. In 2017 IEEE\/ACM 25th International Conference on Program Comprehension (ICPC). 154\u2013164. https:\/\/doi.org\/10.1109\/ICPC.2017.2 10.1109\/ICPC.2017.2","DOI":"10.1109\/ICPC.2017.2"},{"key":"e_1_3_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3548606.3559367"},{"key":"e_1_3_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2019.2940439"},{"key":"e_1_3_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/52.43044"},{"key":"e_1_3_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3428293"},{"key":"e_1_3_1_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2022.102846"},{"key":"e_1_3_1_16_1","unstructured":"Anderson Derek and Randal Scott. 2023. Word ninja. Retrieved June 5 2023 from https:\/\/github.com\/keredson\/wordninja"},{"key":"e_1_3_1_17_1","doi-asserted-by":"publisher","unstructured":"Jacob Devlin Ming-Wei Chang Kenton Lee and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. (2019) 4171\u20134186. https:\/\/doi.org\/10.18653\/V1\/N19-1423 10.18653\/V1\/N19-1423","DOI":"10.18653\/V1\/N19-1423"},{"key":"e_1_3_1_18_1","doi-asserted-by":"publisher","unstructured":"Steven HH Ding Benjamin CM Fung and Philippe Charland. 2019. Asm2Vec: Boosting Static Representation Robustness for Binary Clone Search against Code Obfuscation and Compiler Optimization. In 2019 IEEE Symposium on Security and Privacy (SP). 472\u2013489. https:\/\/doi.org\/10.1109\/SP.2019.00003 10.1109\/SP.2019.00003","DOI":"10.1109\/SP.2019.00003"},{"key":"e_1_3_1_19_1","first-page":"853","volume-title":"2022 USENIX Annual Technical Conference (USENIX ATC 22)","author":"Du Yufei","year":"2022","unstructured":"Yufei Du, Kevin Snow, Fabian Monrose, et al. 2022. Automatic Recovery of Fine-grained Compiler Artifacts at the Binary Level. In 2022 USENIX Annual Technical Conference (USENIX ATC 22). USENIX Association, Carlsbad, CA, 853\u2013868. https:\/\/www.usenix.org\/conference\/atc22\/presentation\/du"},{"key":"e_1_3_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2020.2976920"},{"key":"e_1_3_1_21_1","first-page":"1237","volume-title":"29th USENIX Security Symposium (USENIX Security 20)","author":"Feng Bo","year":"2020","unstructured":"Bo Feng, Alejandro Mera, and Long Lu. 2020. P2IM: Scalable and Hardware-independent Firmware Testing via Automatic Peripheral Interface Modeling. In 29th USENIX Security Symposium (USENIX Security 20). USENIX Association, 1237\u20131254. https:\/\/www.usenix.org\/conference\/usenixsecurity20\/presentation\/feng"},{"key":"e_1_3_1_22_1","doi-asserted-by":"publisher","unstructured":"Patrick Fernandes Miltiadis Allamanis and Marc Brockschmidt. 2019. Structured Neural Summarization. In International Conference on Learning Representations. https:\/\/doi.org\/10.48550\/arXiv.1811.01824 10.48550\/arXiv.1811.01824","DOI":"10.48550\/arXiv.1811.01824"},{"key":"e_1_3_1_23_1","unstructured":"Free Software Foundation. 2023a. Coreutils - gnu core utilities. Retrieved June 5 2023 from https:\/\/www.gnu.org\/software\/coreutils\/"},{"key":"e_1_3_1_24_1","unstructured":"Free Software Foundation. 2023b. Gnu binutilss. Retrieved June 5 2023 from https:\/\/www.gnu.org\/software\/binutils\/"},{"key":"e_1_3_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3460319.3464804"},{"key":"e_1_3_1_26_1","unstructured":"GCC. 2023. Options That Control Optimization. Retrieved June 5 2023 from https:\/\/gcc.gnu.org\/onlinedocs\/gcc\/Optimize-Options.html"},{"key":"e_1_3_1_27_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1603.06393"},{"key":"e_1_3_1_28_1","first-page":"1157","article-title":"An introduction to variable and feature selection","volume":"3","author":"Guyon Isabelle","year":"2003","unstructured":"Isabelle Guyon and Andr\u00e9 Elisseeff. 2003. An introduction to variable and feature selection. Journal of machine learning research 3, Mar (2003), 1157\u20131182.","journal-title":"Journal of machine learning research"},{"key":"e_1_3_1_29_1","unstructured":"Antti Haapala. 2023. Python- Levenshtein. Retrieved June 5 2023 from https:\/\/github.com\/ztane\/python-Levenshtein"},{"key":"e_1_3_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3243734.3243866"},{"key":"e_1_3_1_31_1","unstructured":"Hex-Rays. 2023. IDA Pro. Retrieved June 5 2023 from https:\/\/hex-rays.com\/ida-pro\/"},{"key":"e_1_3_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/2902362"},{"key":"e_1_3_1_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-03013-0_14"},{"key":"e_1_3_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3196321.3196334"},{"key":"e_1_3_1_35_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/314"},{"key":"e_1_3_1_36_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1909.09436"},{"key":"e_1_3_1_37_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1195"},{"key":"e_1_3_1_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-0716-1418-1"},{"key":"e_1_3_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3548606.3560612"},{"key":"e_1_3_1_40_1","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations ICLR 2015 San Diego CA USA May 7-9 2015 Conference Track Proceedings Yoshua Bengio and Yann LeCun (Eds.). http:\/\/arxiv.org\/abs\/1412.6980"},{"key":"e_1_3_1_41_1","doi-asserted-by":"publisher","unstructured":"Anton Kolonin and Vignav Ramesh. 2022. Unsupervised Tokenization Learning. (2022) 3649\u20133664. https:\/\/doi.org\/10.18653\/V1\/2022.EMNLP-MAIN.239 10.18653\/V1\/2022.EMNLP-MAIN.239","DOI":"10.18653\/V1\/2022.EMNLP-MAIN.239"},{"key":"e_1_3_1_42_1","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-57868-4_57"},{"key":"e_1_3_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASE.2019.00064"},{"key":"e_1_3_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3387904.3389268"},{"key":"e_1_3_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2019.00087"},{"key":"e_1_3_1_46_1","doi-asserted-by":"publisher","unstructured":"Mike Lewis Yinhan Liu Naman Goyal Marjan Ghazvininejad Abdelrahman Mohamed Omer Levy Veselin Stoyanov and Luke Zettlemoyer. 2020. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation Translation and Comprehension. (2020) 7871\u20137880. https:\/\/doi.org\/10.18653\/V1\/2020.ACL-MAIN.703 10.18653\/V1\/2020.ACL-MAIN.703","DOI":"10.18653\/V1\/2020.ACL-MAIN.703"},{"key":"e_1_3_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3460120.3484587"},{"key":"e_1_3_1_48_1","doi-asserted-by":"publisher","unstructured":"Yi Li Shaohua Wang and Tien Nguyen. 2021b. A Context-Based Automated Approach for Method Name Consistency Checking and Suggestion. In 2021 IEEE\/ACM 43rd International Conference on Software Engineering (ICSE). 574\u2013586. https:\/\/doi.org\/10.1109\/ICSE43902.2021.00060 10.1109\/ICSE43902.2021.00060","DOI":"10.1109\/ICSE43902.2021.00060"},{"key":"e_1_3_1_49_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-30164-8_306"},{"key":"e_1_3_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3133908"},{"key":"e_1_3_1_51_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1301.3781"},{"key":"e_1_3_1_52_1","doi-asserted-by":"publisher","DOI":"10.5555\/3104322.3104425"},{"key":"e_1_3_1_53_1","unstructured":"OpenAI. 2023. GPT-4. Retrieved June 5 2023 from https:\/\/platform.openai.com\/docs\/models\/gpt-4"},{"key":"e_1_3_1_54_1","article-title":"Pytorch: An imperative style, high-performance deep learning library","volume":"32","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019).","journal-title":"Advances in neural information processing systems"},{"key":"e_1_3_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3427228.3427265"},{"key":"e_1_3_1_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2022.3231621"},{"key":"e_1_3_1_57_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11431-020-1647-3"},{"issue":"2","key":"e_1_3_1_58_1","first-page":"2","article-title":"Gensim\u2013python framework for vector space modelling","volume":"3","author":"Rehurek Radim","year":"2011","unstructured":"Radim Rehurek and Petr Sojka. 2011. Gensim\u2013python framework for vector space modelling. NLP Centre, Faculty of Informatics, Masaryk University, Brno, Czech Republic 3, 2 (2011), 2.","journal-title":"NLP Centre, Faculty of Informatics, Masaryk University, Brno, Czech Republic"},{"key":"e_1_3_1_59_1","unstructured":"Wind River. 2023. VxWorks:The Leading RTOS for the Intelligent Edge. Retrieved June 5 2023 from https:\/\/www.windriver.com\/products\/vxworks"},{"key":"e_1_3_1_60_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1025667309714"},{"key":"e_1_3_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3104029"},{"key":"e_1_3_1_62_1","doi-asserted-by":"publisher","unstructured":"Florian Schroff Dmitry Kalenichenko and James Philbin. 2015. Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition. 815\u2013823. https:\/\/doi.org\/10.1109\/CVPR.2015.7298682 10.1109\/CVPR.2015.7298682","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"e_1_3_1_63_1","unstructured":"CEA IT Security. 2023. Miasm. Retrieved June 5 2023 from https:\/\/github.com\/cea-sec\/miasm"},{"key":"e_1_3_1_64_1","first-page":"6947","volume-title":"Proceedings of the Twelfth Language Resources and Evaluation Conference","author":"Seeha Suteera","year":"2020","unstructured":"Suteera Seeha, Ivan Bilan, Liliana Mamani Sanchez, Johannes Huber, Michael Matuschek, and Hinrich Sch\u00fctze. 2020. ThaiLMCut: Unsupervised Pretraining for Thai Word Segmentation. In Proceedings of the Twelfth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, 6947\u20136957. https:\/\/aclanthology.org\/2020.lrec-1.858"},{"key":"e_1_3_1_65_1","doi-asserted-by":"publisher","DOI":"10.1016\/0022-2836(81)90087-5"},{"key":"e_1_3_1_66_1","unstructured":"Synopsys. 2022. Synopsys 2022 open source security and risk analysis report. Retrieved 2023 from https:\/\/www.synopsys.com\/software-integrity\/resources\/analyst-reports\/open-source-security-risk-analysis.html"},{"key":"e_1_3_1_67_1","unstructured":"Princeton University. 2023. WordNet A Lexical Database for English. Retrieved June 5 2023 from https:\/\/wordnet.princeton.edu\/"},{"key":"e_1_3_1_68_1","first-page":"1875","volume-title":"29th USENIX Security Symposium (USENIX Security 20)","author":"Votipka Daniel","year":"2020","unstructured":"Daniel Votipka, Seth Rabin, Kristopher Micinski, Jeffrey S. Foster, and Michelle L. Mazurek. 2020. An Observational Investigation of Reverse Engineers\u2019 Processes. In 29th USENIX Security Symposium (USENIX Security 20). USENIX Association, 1875\u20131892. https:\/\/www.usenix.org\/conference\/usenixsecurity20\/presentation\/votipka-observational"},{"key":"e_1_3_1_69_1","doi-asserted-by":"publisher","unstructured":"Daniel Votipka Rock Stevens Elissa Redmiles Jeremy Hu and Michelle Mazurek. 2018. Hackers vs. Testers: A Comparison of Software Vulnerability Discovery Processes. In 2018 IEEE Symposium on Security and Privacy (SP). 374\u2013391. https:\/\/doi.org\/10.1109\/SP.2018.00003 10.1109\/SP.2018.00003","DOI":"10.1109\/SP.2018.00003"},{"key":"e_1_3_1_70_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_1_71_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/552"},{"key":"e_1_3_1_72_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2306.02546"},{"key":"e_1_3_1_73_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2016.18"},{"key":"e_1_3_1_74_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2021.107354"},{"key":"e_1_3_1_75_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSN48987.2021.00036"},{"key":"e_1_3_1_76_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5466"},{"key":"e_1_3_1_77_1","doi-asserted-by":"publisher","unstructured":"Xiaoling Zhang Shouguo Yang Luqian Duan Zhe Lang Zhiqiang Shi and Limin Sun. 2021. Transformer-XL With Graph Neural Network for Source Code Summarization. In 2021 IEEE International Conference on Systems Man and Cybernetics (SMC). 3436\u20133441. https:\/\/doi.org\/10.1109\/SMC52423.2021.9658619 10.1109\/SMC52423.2021.9658619","DOI":"10.1109\/SMC52423.2021.9658619"}],"container-title":["Proceedings of the ACM on Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3660782","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3660782","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T07:56:13Z","timestamp":1770191773000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3660782"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,12]]},"references-count":76,"journal-issue":{"issue":"FSE","published-print":{"date-parts":[[2024,7,12]]}},"alternative-id":["10.1145\/3660782"],"URL":"https:\/\/doi.org\/10.1145\/3660782","relation":{},"ISSN":["2994-970X"],"issn-type":[{"value":"2994-970X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,12]]}}}