{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T14:33:23Z","timestamp":1754145203595,"version":"3.41.2"},"reference-count":66,"publisher":"Association for Computing Machinery (ACM)","issue":"ISSTA","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Softw. Eng."],"published-print":{"date-parts":[[2025,6,22]]},"abstract":"<jats:p>While existing machine learning (ML) frameworks focus on established platforms, like running CUDA on server-grade GPUs, there have been growing demands to enable emerging AI applications in a broader set of scenarios, such as running Large Language Models (LLMs) within browsers and mobile phones. However, deploying emerging models on new platforms (such as Metal and WebGPU) presents significant software engineering challenges due to rapid model evolution and limited tooling and practices for these platforms.  \nPrevious practice for ML model deployment often follows a bottom-up fashion, where engineers first implement individual required operators and then put them together. However, this traditional development approach fails to meet the productivity requirements when deploying emerging ML applications, with the testing and debugging part as a bottleneck. To this end, we introduce TapML, a top-down approach designed to streamline model deployment on diverse platforms. While the traditional bottom-up approach requires crafting manual tests, TapML automatically creates high-quality, realistic test data through operator-wise test carving. Furthermore, TapML uses a migration-based strategy to gradually offload model implementation from the mature source platform to the target platform, minimizing the debugging scope of compound errors.  \nTapML has been used as the default development method in the MLC-LLM project to deploy emerging ML models. Within 2 years, TapML has accelerated the deployment of 105 emerging models in 27 model architectures across 5 emerging platforms. We show that TapML effectively boosts developer productivity while ensuring the quality of deployed models. Furthermore, we summarize comprehensive case studies from our real-world development, offering best practices for developing emerging ML systems.<\/jats:p>","DOI":"10.1145\/3728957","type":"journal-article","created":{"date-parts":[[2025,6,22]],"date-time":"2025-06-22T10:52:56Z","timestamp":1750589576000},"page":"1818-1840","source":"Crossref","is-referenced-by-count":0,"title":["Productively Deploying Emerging Models on Emerging Platforms: A Top-Down Approach for Testing and Debugging"],"prefix":"10.1145","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4682-983X","authenticated-orcid":false,"given":"Siyuan","family":"Feng","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7122-8625","authenticated-orcid":false,"given":"Jiawei","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Illinois Urbana-Champaign, Urbana, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6400-5079","authenticated-orcid":false,"given":"Ruihang","family":"Lai","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5369-4593","authenticated-orcid":false,"given":"Charlie","family":"Ruan","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0281-8271","authenticated-orcid":false,"given":"Yong","family":"Yu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5175-2702","authenticated-orcid":false,"given":"Lingming","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Illinois Urbana-Champaign, Urbana, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5744-3940","authenticated-orcid":false,"given":"Tianqi","family":"Chen","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,6,22]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"12th USENIX symposium on operating systems design and implementation (OSDI 16)","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, and Michael Isard. 2016. Tensorflow: A system for large-scale machine learning. In 12th USENIX symposium on operating systems design and implementation (OSDI 16). 265\u2013283."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3470496.3527405"},{"key":"e_1_2_1_3_1","volume-title":"Diogo Almeida, Janko Altenschmidt, Sam Altman, and Shyamal Anadkat.","author":"Achiam Josh","year":"2023","unstructured":"Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, and Shyamal Anadkat. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774."},{"key":"e_1_2_1_4_1","unstructured":"AMD. 2024. AMD ROCm Software. https:\/\/www.amd.com\/en\/products\/software\/rocm.html"},{"key":"e_1_2_1_5_1","unstructured":"Anthropic. 2024. Introducing Computer Use a New Claude 3.5 Sonnet and Claude 3.5 Haiku. https:\/\/www.anthropic.com\/news\/3-5-models-and-computer-use Accessed: 2024-11-01"},{"key":"e_1_2_1_6_1","unstructured":"Apple. 2024. Metal Overview - Apple Developer. https:\/\/developer.apple.com\/metal\/"},{"key":"e_1_2_1_7_1","volume-title":"ONNX: Open Neural Network Exchange. https:\/\/github.com\/onnx\/onnx","author":"Bai Junjie","year":"2019","unstructured":"Junjie Bai, Fang Lu, and Ke Zhang. 2019. ONNX: Open Neural Network Exchange. https:\/\/github.com\/onnx\/onnx"},{"key":"e_1_2_1_8_1","volume-title":"Ho Young Jhoo, and Juneyoung Lee","author":"Bang Seongwon","year":"2022","unstructured":"Seongwon Bang, Seunghyeon Nam, Inwhan Chun, Ho Young Jhoo, and Juneyoung Lee. 2022. SMT-Based Translation Validation for Machine Learning Compiler. In Computer Aided Verification, Sharon Shoham and Yakir Vizel (Eds.). Springer International Publishing, Cham. 386\u2013407. isbn:978-3-031-13188-2"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3183440.3195019"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASE.2019.00016"},{"key":"e_1_2_1_11_1","volume-title":"Improving image generation with better captions. Computer Science. https:\/\/cdn. openai. com\/papers\/dall-e-3. pdf, 2, 3","author":"Betker James","year":"2023","unstructured":"James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, and Yufei Guo. 2023. Improving image generation with better captions. Computer Science. https:\/\/cdn. openai. com\/papers\/dall-e-3. pdf, 2, 3 (2023), 8."},{"key":"e_1_2_1_12_1","unstructured":"Andreas Blattmann Tim Dockhorn Sumith Kulal Daniel Mendelevitch Maciej Kilian Dominik Lorenz Yam Levi Zion English Vikram Voleti and Adam Letts. 2023. Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv preprint arXiv:2311.15127."},{"key":"e_1_2_1_13_1","volume-title":"13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18)","author":"Chen Tianqi","year":"2018","unstructured":"Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Haichen Shen, Meghan Cowan, Leyuan Wang, Yuwei Hu, and Luis Ceze. 2018. TVM: An automated end-to-end optimizing compiler for deep learning. In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18). 578\u2013594."},{"key":"e_1_2_1_14_1","doi-asserted-by":"crossref","unstructured":"Yanzuo Chen Yuanyuan Yuan and Shuai Wang. 2023. OBSan: An Out-Of-Bound Sanitizer to Harden DNN Executables.. In NDSS.","DOI":"10.14722\/ndss.2023.24103"},{"key":"e_1_2_1_15_1","unstructured":"World Wide Web Consortium. 2024. WebGPU. https:\/\/www.w3.org\/TR\/webgpu\/"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3597926.3598067"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3540250.3549085"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/1181775.1181806"},{"key":"e_1_2_1_19_1","volume-title":"The 6th Joint Meeting on European software engineering conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering: Companion Papers. 549\u2013552","author":"Evans Robert B","year":"2007","unstructured":"Robert B Evans and Alberto Savoia. 2007. Differential testing: a new approach to change detection. In The 6th Joint Meeting on European software engineering conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering: Companion Papers. 549\u2013552."},{"key":"e_1_2_1_20_1","volume-title":"International Conference on Model Driven Engineering Languages and Systems. 482\u2013497","author":"Fleurey Franck","year":"2007","unstructured":"Franck Fleurey, Erwan Breton, Benoit Baudry, Alain Nicolas, and Jean-Marc J\u00e9z\u00e9quel. 2007. Model-driven engineering for software migration in a large industrial context. In International Conference on Model Driven Engineering Languages and Systems. 482\u2013497."},{"key":"e_1_2_1_21_1","unstructured":"Georgi Gerganov. 2023. llama.cpp. https:\/\/github.com\/ggerganov\/llama.cpp"},{"key":"e_1_2_1_22_1","unstructured":"Google. 2024. tensorflow\/tfjs: A WebGL accelerated JavaScript library for training and deploying ML models.. https:\/\/github.com\/tensorflow\/tfjs"},{"key":"e_1_2_1_23_1","volume-title":"Allie Del Giorno, Sivakanth Gopi, Mojan Javaheripi, Piero Kauffmann, Gustavo de Rosa, and Olli Saarikivi.","author":"Gunasekar Suriya","year":"2023","unstructured":"Suriya Gunasekar, Yi Zhang, Jyoti Aneja, Caio C\u00e9sar Teodoro Mendes, Allie Del Giorno, Sivakanth Gopi, Mojan Javaheripi, Piero Kauffmann, Gustavo de Rosa, and Olli Saarikivi. 2023. Textbooks Are All You Need. arXiv preprint arXiv:2306.11644."},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/C-M.1978.218134"},{"key":"e_1_2_1_25_1","unstructured":"Apple Inc.. 2024. Apple Intelligence. https:\/\/www.apple.com\/apple-intelligence\/ Accessed: 2024-11-01"},{"key":"e_1_2_1_26_1","volume-title":"Diego de las Casas, Emma Bou Hanna, and Florian Bressand.","author":"Jiang Albert Q","year":"2024","unstructured":"Albert Q Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, and Florian Bressand. 2024. Mixtral of experts. arXiv preprint arXiv:2401.04088."},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3579371.3589350"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3600006.3613165"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3676641.3716249"},{"key":"e_1_2_1_30_1","volume-title":"MLIR: A compiler infrastructure for the end of Moore\u2019s law. arXiv preprint arXiv:2002.11054.","author":"Lattner Chris","year":"2020","unstructured":"Chris Lattner, Mehdi Amini, Uday Bondhugula, Albert Cohen, Andy Davis, Jacques Pienaar, River Riddle, Tatiana Shpeisman, Nicolas Vasilache, and Oleksandr Zinenko. 2020. MLIR: A compiler infrastructure for the end of Moore\u2019s law. arXiv preprint arXiv:2002.11054."},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/QRS60937.2023.00066"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3575693.3575707"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3611643.3616337"},{"key":"e_1_2_1_34_1","volume-title":"Yuyao Wang, and Lingming Zhang.","author":"Liu Jiawei","year":"2024","unstructured":"Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, and Lingming Zhang. 2024. Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation. Advances in Neural Information Processing Systems, 36 (2024)."},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2008.4541126"},{"key":"e_1_2_1_36_1","volume-title":"Ming Guang Yong, and Juhyun Lee","author":"Lugaresi Camillo","year":"2019","unstructured":"Camillo Lugaresi, Jiuqiang Tang, Hadon Nash, Chris McClanahan, Esha Uboweja, Michael Hays, Fan Zhang, Chuo-Ling Chang, Ming Guang Yong, and Juhyun Lee. 2019. Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172."},{"key":"e_1_2_1_37_1","first-page":"100","article-title":"Differential testing for software","volume":"10","author":"McKeeman William M","year":"1998","unstructured":"William M McKeeman. 1998. Differential testing for software. Digital Technical Journal, 10, 1 (1998), 100\u2013107.","journal-title":"Digital Technical Journal"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/96267.96279"},{"key":"e_1_2_1_39_1","unstructured":"MLC team. 2023-2025. MLC-LLM. https:\/\/github.com\/mlc-ai\/mlc-llm"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/HOTCHIPS.2009.7478342"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/2642937.2643010"},{"key":"e_1_2_1_42_1","volume-title":"Polygraphy: A Deep Learning Inference Prototyping and Debugging Toolkit. https:\/\/github.com\/NVIDIA\/TensorRT\/tree\/main\/tools\/Polygraphy","author":"NVIDIA.","year":"2022","unstructured":"NVIDIA. 2022. Polygraphy: A Deep Learning Inference Prototyping and Debugging Toolkit. https:\/\/github.com\/NVIDIA\/TensorRT\/tree\/main\/tools\/Polygraphy"},{"key":"e_1_2_1_43_1","unstructured":"NVIDIA. 2022. NVIDIA TensorRT. https:\/\/developer.nvidia.com\/tensorrt"},{"key":"e_1_2_1_44_1","unstructured":"NVIDIA. 2024. TensorRT-LLM. https:\/\/github.com\/NVIDIA\/TensorRT-LLM"},{"key":"e_1_2_1_45_1","unstructured":"OpenAI. 2024. Video generation models as world simulators. https:\/\/openai.com\/research\/video-generation-models-as-world-simulators"},{"key":"e_1_2_1_46_1","unstructured":"Orange Pi. 2024. Orange Pi - Orangepi. http:\/\/www.orangepi.org\/html\/hardWare\/computerAndMicrocontrollers\/details\/Orange-Pi-5.html"},{"key":"e_1_2_1_47_1","volume-title":"Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 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, and Luca Antiga. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32 (2019), 8026\u20138037."},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132747.3132785"},{"key":"e_1_2_1_49_1","volume-title":"RWKV: Reinventing RNNs for the Transformer Era. arXiv preprint arXiv:2305.13048.","author":"Peng Bo","year":"2023","unstructured":"Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Huanqi Cao, Xin Cheng, Michael Chung, Matteo Grella, and Kranthi Kiran GV. 2023. RWKV: Reinventing RNNs for the Transformer Era. arXiv preprint arXiv:2305.13048."},{"key":"e_1_2_1_50_1","volume-title":"Sdxl: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952.","author":"Podell Dustin","year":"2023","unstructured":"Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas M\u00fcller, Joe Penna, and Robin Rombach. 2023. Sdxl: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952."},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3293882.3330575"},{"key":"e_1_2_1_52_1","first-page":"36479","article-title":"Photorealistic text-to-image diffusion models with deep language understanding","volume":"35","author":"Saharia Chitwan","year":"2022","unstructured":"Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, and Tim Salimans. 2022. Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Information Processing Systems, 35 (2022), 36479\u201336494.","journal-title":"Advances in Neural Information Processing Systems"},{"volume-title":"Vulkan programming guide: The official guide to learning vulkan","author":"Sellers Graham","key":"e_1_2_1_53_1","unstructured":"Graham Sellers and John Kessenich. 2016. Vulkan programming guide: The official guide to learning vulkan. Addison-Wesley Professional."},{"key":"e_1_2_1_54_1","volume-title":"TorchProbe: Fuzzing Dynamic Deep Learning Compilers. In Asian Symposium on Programming Languages and Systems. 310\u2013331","author":"Su Qidong","year":"2023","unstructured":"Qidong Su, Chuqin Geng, Gennady Pekhimenko, and Xujie Si. 2023. TorchProbe: Fuzzing Dynamic Deep Learning Compilers. In Asian Symposium on Programming Languages and Systems. 310\u2013331."},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3315508.3329973"},{"key":"e_1_2_1_56_1","unstructured":"Hugo Touvron Louis Martin Kevin Stone Peter Albert Amjad Almahairi Yasmine Babaei Nikolay Bashlykov Soumya Batra Prajjwal Bhargava and Shruti Bhosale. 2023. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288."},{"key":"e_1_2_1_57_1","volume-title":"MLIRSmith: Random Program Generation for Fuzzing MLIR Compiler Infrastructure. In 2023 38th IEEE\/ACM International Conference on Automated Software Engineering (ASE). 1555\u20131566","author":"Wang Haoyu","year":"2023","unstructured":"Haoyu Wang, Junjie Chen, Chuyue Xie, Shuang Liu, Zan Wang, Qingchao Shen, and Yingquan Zhao. 2023. MLIRSmith: Random Program Generation for Fuzzing MLIR Compiler Infrastructure. In 2023 38th IEEE\/ACM International Conference on Automated Software Engineering (ASE). 1555\u20131566."},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510165"},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/3597926.3598105"},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510041"},{"key":"e_1_2_1_61_1","volume-title":"Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush.","author":"Wolf Thomas","year":"2020","unstructured":"Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Perric Cistac, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020. Transformers: State-of-the-Art Natural Language Processing. Association for Computational Linguistics, 38\u201345. https:\/\/www.aclweb.org\/anthology\/2020.emnlp-demos.6"},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/3533767.3534220"},{"key":"e_1_2_1_63_1","volume-title":"2023 IEEE\/ACM 45th International Conference on Software Engineering (ICSE). 1174\u20131186","author":"Yang Chenyuan","year":"2023","unstructured":"Chenyuan Yang, Yinlin Deng, Jiayi Yao, Yuxing Tu, Hanchi Li, and Lingming Zhang. 2023. Fuzzing automatic differentiation in deep-learning libraries. In 2023 IEEE\/ACM 45th International Conference on Software Engineering (ICSE). 1174\u20131186."},{"key":"e_1_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/32.988498"},{"key":"e_1_2_1_65_1","volume-title":"Shiyi Cao, Christos Kozyrakis, Ion Stoica, and Joseph E Gonzalez.","author":"Zheng Lianmin","year":"2023","unstructured":"Lianmin Zheng, Liangsheng Yin, Zhiqiang Xie, Jeff Huang, Chuyue Sun, Cody Hao Yu, Shiyi Cao, Christos Kozyrakis, Ion Stoica, and Joseph E Gonzalez. 2023. Efficiently programming large language models using sglang. arXiv e-prints, arXiv\u20132312."},{"key":"e_1_2_1_66_1","volume-title":"Proceedings of the 32nd ACM\/IEEE International Conference on Software Engineering-Volume 1. 195\u2013204","author":"Zhong Hao","year":"2010","unstructured":"Hao Zhong, Suresh Thummalapenta, Tao Xie, Lu Zhang, and Qing Wang. 2010. Mining API mapping for language migration. In Proceedings of the 32nd ACM\/IEEE International Conference on Software Engineering-Volume 1. 195\u2013204."}],"container-title":["Proceedings of the ACM on Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3728957","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T16:48:45Z","timestamp":1752684525000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3728957"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,22]]},"references-count":66,"journal-issue":{"issue":"ISSTA","published-print":{"date-parts":[[2025,6,22]]}},"alternative-id":["10.1145\/3728957"],"URL":"https:\/\/doi.org\/10.1145\/3728957","relation":{},"ISSN":["2994-970X"],"issn-type":[{"type":"electronic","value":"2994-970X"}],"subject":[],"published":{"date-parts":[[2025,6,22]]}}}