{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T18:40:39Z","timestamp":1778092839361,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":73,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T00:00:00Z","timestamp":1712880000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["No.62232016"],"award-info":[{"award-number":["No.62232016"]}]},{"name":"National Natural Science Foundation of China","award":["No.62072442"],"award-info":[{"award-number":["No.62072442"]}]},{"name":"National Natural Science Foundation of China","award":["No.62272445"],"award-info":[{"award-number":["No.62272445"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,4,12]]},"DOI":"10.1145\/3597503.3639118","type":"proceedings-article","created":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T16:43:26Z","timestamp":1712940206000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":38,"title":["Testing the Limits: Unusual Text Inputs Generation for Mobile App Crash Detection with Large Language Model"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9709-8275","authenticated-orcid":false,"given":"Zhe","family":"Liu","sequence":"first","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing, China"},{"name":"Institute of Software, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2011-9618","authenticated-orcid":false,"given":"Chunyang","family":"Chen","sequence":"additional","affiliation":[{"name":"Technical University of Munich, Munich, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9941-6713","authenticated-orcid":false,"given":"Junjie","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Software, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4397-750X","authenticated-orcid":false,"given":"Mengzhuo","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Software, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9285-3419","authenticated-orcid":false,"given":"Boyu","family":"Wu","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6189-7659","authenticated-orcid":false,"given":"Zhilin","family":"Tian","sequence":"additional","affiliation":[{"name":"Pennsylvania State University, State College, Pennsylvania, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2762-9223","authenticated-orcid":false,"given":"Yuekai","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1530-7499","authenticated-orcid":false,"given":"Jun","family":"Hu","sequence":"additional","affiliation":[{"name":"Institute of Software, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2618-5694","authenticated-orcid":false,"given":"Qing","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Software, Chinese Academy of Sciences, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2024,4,12]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"2022. Crash bug text. https:\/\/www.theguardian.com\/technology\/iphone-crash-bug-text-imessage-ios."},{"key":"e_1_3_2_1_2_1","unstructured":"2022. Crash bug text in ios. https:\/\/tech.hindustantimes.com\/tech\/news\/be-careful-a-new-text-bomb-is-making-whatsapp-crash-and-will-hang-your-phone-71599532897852.html."},{"key":"e_1_3_2_1_3_1","unstructured":"2022. F-droid. https:\/\/f-droid.org\/."},{"key":"e_1_3_2_1_4_1","volume-title":"Few-shot training LLMs for project-specific code-summarization. ASE","author":"Ahmed Toufique","year":"2022","unstructured":"Toufique Ahmed and Premkumar Devanbu. 2022. Few-shot training LLMs for project-specific code-summarization. ASE (2022)."},{"key":"e_1_3_2_1_5_1","volume-title":"Automated web application testing using search based software engineering","author":"Alshahwan Nadia","unstructured":"Nadia Alshahwan and Mark Harman. 2011. Automated web application testing using search based software engineering. In ASE. IEEE, 3--12."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2393596.2393666"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/APSEC.2016.036"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/spe.2564"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2509136.2509549"},{"key":"e_1_3_2_1_10_1","unstructured":"Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et al. 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020) 1877--1901."},{"key":"e_1_3_2_1_11_1","unstructured":"Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et al. 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020) 1877--1901."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3387903.3389308"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2018.02.002"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3498707"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59152-6_18"},{"key":"e_1_3_2_1_16_1","volume-title":"Big self-supervised models are strong semi-supervised learners. Advances in neural information processing systems 33","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey E Hinton. 2020. Big self-supervised models are strong semi-supervised learners. Advances in neural information processing systems 33 (2020), 22243--22255."},{"key":"e_1_3_2_1_17_1","volume-title":"Charles Sutton, Sebastian Gehrmann, et al.","author":"Chowdhery Aakanksha","year":"2022","unstructured":"Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2022. Palm: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311 (2022)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-30806-3_8"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3126594.3126651"},{"key":"e_1_3_2_1_20_1","volume-title":"Chenyuan Yang, Shizhuo Dylan Zhang, Shujing Yang, and Lingming Zhang.","author":"Deng Yinlin","year":"2023","unstructured":"Yinlin Deng, Chunqiu Steven Xia, Chenyuan Yang, Shizhuo Dylan Zhang, Shujing Yang, and Lingming Zhang. 2023. Large Language Models are Edge-Case Fuzzers: Testing Deep Learning Libraries via FuzzGPT. ISSTA (2023)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3540250.3549085"},{"key":"e_1_3_2_1_22_1","unstructured":"Android Developers. 2012. Ui\/application exerciser monkey."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3377811.3380402"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISSRE.2013.6698888"},{"key":"e_1_3_2_1_25_1","first-page":"30583","article-title":"What can transformers learn in-context? a case study of simple function classes","volume":"35","author":"Garg Shivam","year":"2022","unstructured":"Shivam Garg, Dimitris Tsipras, Percy S Liang, and Gregory Valiant. 2022. What can transformers learn in-context? a case study of simple function classes. Advances in Neural Information Processing Systems 35 (2022), 30583--30598.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2019.00042"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP40000.2020.00071"},{"key":"e_1_3_2_1_28_1","volume-title":"Proceedings of the ACM on Programming Languages 2, POPL","author":"Holik Lukav","year":"2017","unstructured":"Lukav Holik, Petr Jank, Anthony W Lin, Philipp Rmmer, and Tom Vojnar. 2017. String constraints with concatenation and transducers solved efficiently. Proceedings of the ACM on Programming Languages 2, POPL (2017), 1--32."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASE.2019.00044"},{"key":"e_1_3_2_1_30_1","unstructured":"Text input. 2022. Introduction about text input on Android Developer website. https:\/\/developer.android.google.cn\/reference\/android\/widget\/EditText?hl=en."},{"key":"e_1_3_2_1_31_1","volume-title":"Impact of Code Language Models on Automated Program Repair. ICSE","author":"Jiang Nan","year":"2023","unstructured":"Nan Jiang, Kevin Liu, Thibaud Lutellier, and Lin Tan. 2023. Impact of Code Language Models on Automated Program Repair. ICSE (2023)."},{"key":"e_1_3_2_1_32_1","volume-title":"Large Language Models are Few-shot Testers: Exploring LLM-based General Bug Reproduction. ICSE","author":"Kang Sungmin","year":"2023","unstructured":"Sungmin Kang, Juyeon Yoon, and Shin Yoo. 2023. Large Language Models are Few-shot Testers: Exploring LLM-based General Bug Reproduction. ICSE (2023)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/2377656.2377662"},{"key":"e_1_3_2_1_34_1","volume-title":"Declarative Programming and Knowledge Management: Conference on Declarative Programming, DECLARE","author":"Krings Sebastian","year":"2019","unstructured":"Sebastian Krings, Joshua Schmidt, Patrick Skowronek, Jannik Dunkelau, and Dierk Ehmke. 2020. Towards constraint logic programming over strings for test data generation. In Declarative Programming and Knowledge Management: Conference on Declarative Programming, DECLARE 2019, Unifying INAP, WLP, and WFLP, Cottbus, Germany, September 9--12, 2019, Revised Selected Papers 22. Springer, 139--159."},{"key":"e_1_3_2_1_35_1","volume-title":"Reinforcement learning with augmented data. Advances in neural information processing systems 33","author":"Laskin Misha","year":"2020","unstructured":"Misha Laskin, Kimin Lee, Adam Stooke, Lerrel Pinto, Pieter Abbeel, and Aravind Srinivas. 2020. Reinforcement learning with augmented data. Advances in neural information processing systems 33 (2020), 19884--19895."},{"key":"e_1_3_2_1_36_1","volume-title":"Shuvendu K Lahiri, and Siddhartha Sen.","author":"Lemieux Caroline","year":"2023","unstructured":"Caroline Lemieux, Jeevana Priya Inala, Shuvendu K Lahiri, and Siddhartha Sen. 2023. CODAMOSA: Escaping Coverage Plateaus in Test Generation with Pretrained Large Language Models. In ICSE."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-03077-7_2"},{"key":"e_1_3_2_1_38_1","volume-title":"DroidBot: A Lightweight UI-Guided Test Input Generator for Android (ICSE-C '17)","author":"Li Yuanchun","year":"2017","unstructured":"Yuanchun Li, Ziyue Yang, Yao Guo, and Xiangqun Chen. 2017. DroidBot: A Lightweight UI-Guided Test Input Generator for Android (ICSE-C '17)."},{"key":"e_1_3_2_1_39_1","volume-title":"2017 IEEE\/ACM 39th International Conference on Software Engineering Companion (ICSE-C). IEEE, 23--26","author":"Li Yuanchun","year":"2017","unstructured":"Yuanchun Li, Ziyue Yang, Yao Guo, and Xiangqun Chen. 2017. Droidbot: a lightweight ui-guided test input generator for android. In 2017 IEEE\/ACM 39th International Conference on Software Engineering Companion (ICSE-C). IEEE, 23--26."},{"key":"e_1_3_2_1_40_1","volume-title":"Humanoid: a deep learning-based approach to automated black-box Android app testing","author":"Li Yuanchun","unstructured":"Yuanchun Li, Ziyue Yang, Yao Guo, and Xiangqun Chen. 2019. Humanoid: a deep learning-based approach to automated black-box Android app testing. In ASE. IEEE, 1070--1073."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2018.2834476"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-08867-9_43"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2017.65"},{"key":"e_1_3_2_1_44_1","volume-title":"Fill in the Blank: Context-aware Automated Text Input Generation for Mobile GUI Testing. arXiv preprint arXiv:2212.04732","author":"Liu Zhe","year":"2022","unstructured":"Zhe Liu, Chunyang Chen, Junjie Wang, Xing Che, Yuekai Huang, Jun Hu, and Qing Wang. 2022. Fill in the Blank: Context-aware Automated Text Input Generation for Mobile GUI Testing. arXiv preprint arXiv:2212.04732 (2022)."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3324884.3416547"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2022.3150876"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE48619.2023.00168"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3510454.3516848"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3491102.3501903"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.nuse-1.5"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/2491411.2491450"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/2931037.2931054"},{"key":"e_1_3_2_1_53_1","volume-title":"Efficient Estimation of Word Representations in Vector Space. Computer Science","author":"Mikolov Tomas","year":"2013","unstructured":"Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. Computer Science (2013)."},{"key":"e_1_3_2_1_54_1","volume-title":"Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? arXiv preprint arXiv:2202.12837","author":"Min Sewon","year":"2022","unstructured":"Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022. Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? arXiv preprint arXiv:2202.12837 (2022)."},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE48619.2023.00205"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3395363.3397354"},{"key":"e_1_3_2_1_57_1","volume-title":"Synchromesh: Reliable code generation from pre-trained language models. ICLR","author":"Poesia Gabriel","year":"2022","unstructured":"Gabriel Poesia, Oleksandr Polozov, Vu Le, Ashish Tiwari, Gustavo Soares, Christopher Meek, and Sumit Gulwani. 2022. Synchromesh: Reliable code generation from pre-trained language models. ICLR (2022)."},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSTW.2017.21"},{"key":"e_1_3_2_1_59_1","article-title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","volume":"21","author":"Raffel Colin","year":"2020","unstructured":"Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. J. Mach. Learn. Res. 21 (2020), 140:1--140:67.","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/2435349.2435379"},{"key":"e_1_3_2_1_61_1","unstructured":"J Schulman B Zoph C Kim J Hilton J Menick J Weng JFC Uribe L Fedus L Metz M Pokorny et al. 2022. ChatGPT: Optimizing language models for dialogue."},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/32.799955"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40593-022-00300-7"},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/3106237.3106298"},{"key":"e_1_3_2_1_65_1","volume-title":"Automated web application testing driven by pre-recorded test cases. Journal of Systems and Software","author":"Sunman Nezih","year":"2022","unstructured":"Nezih Sunman, Yi\u011fit Soydan, and Hasan S\u00f6zer. 2022. Automated web application testing driven by pre-recorded test cases. Journal of Systems and Software (2022), 111441."},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1145\/2660267.2660372"},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-63390-9_21"},{"key":"e_1_3_2_1_68_1","unstructured":"UIAutomator. 2021. Python wrapper of Android uiautomator test tool. https:\/\/github.com\/xiaocong\/uiautomator."},{"key":"e_1_3_2_1_69_1","volume-title":"Attention is all you need. Advances in neural information processing systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems (2017)."},{"key":"e_1_3_2_1_70_1","doi-asserted-by":"crossref","unstructured":"Jue Wang Yanyan Jiang Chang Xu Chun Cao Xiaoxing Ma and Jian Lu. 2020. Combodroid: generating high-quality test inputs for android apps via use case combinations. In ICSE. 469--480.","DOI":"10.1145\/3377811.3380382"},{"key":"e_1_3_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i3.20215"},{"key":"e_1_3_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1145\/3533767.3534390"},{"key":"e_1_3_2_1_73_1","volume-title":"Xi Victoria Lin, et al","author":"Zhang Susan","year":"2022","unstructured":"Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al. 2022. Opt: Open pre-trained transformer language models. arXiv preprint arXiv:2205.01068 (2022)."}],"event":{"name":"ICSE '24: IEEE\/ACM 46th International Conference on Software Engineering","location":"Lisbon Portugal","acronym":"ICSE '24","sponsor":["SIGSOFT ACM Special Interest Group on Software Engineering","IEEE CS","Faculty of Engineering of University of Porto"]},"container-title":["Proceedings of the IEEE\/ACM 46th International Conference on Software Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3597503.3639118","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3597503.3639118","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:49:12Z","timestamp":1750286952000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3597503.3639118"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,12]]},"references-count":73,"alternative-id":["10.1145\/3597503.3639118","10.1145\/3597503"],"URL":"https:\/\/doi.org\/10.1145\/3597503.3639118","relation":{},"subject":[],"published":{"date-parts":[[2024,4,12]]},"assertion":[{"value":"2024-04-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}