{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T12:17:42Z","timestamp":1783167462629,"version":"3.54.6"},"reference-count":41,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172407"],"award-info":[{"award-number":["62172407"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004739","name":"Chinese Academy of Sciences Youth Innovation Promotion Association","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004739","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010166","name":"Xiamen City Department of Science and Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information and Software Technology"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.infsof.2026.108195","type":"journal-article","created":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T15:06:08Z","timestamp":1779116768000},"page":"108195","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Effective Python-frontend fuzzing for deep learning libraries with runtime coverage feedback"],"prefix":"10.1016","volume":"197","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8808-8038","authenticated-orcid":false,"given":"Xu","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ningning","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liwei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruibang","family":"You","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9048-8887","authenticated-orcid":false,"given":"Qizhen","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruiping","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.infsof.2026.108195_b1","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1002\/rob.21918","article-title":"A survey of deep learning techniques for autonomous driving","volume":"37","author":"Grigorescu","year":"2019","journal-title":"J. Field Robot."},{"issue":"8","key":"10.1016\/j.infsof.2026.108195_b2","doi-asserted-by":"crossref","first-page":"6469","DOI":"10.1109\/JIOT.2020.3043716","article-title":"Computing systems for autonomous driving: State of the art and challenges","volume":"8","author":"Liu","year":"2021","journal-title":"IEEE Internet Things J."},{"issue":"7","key":"10.1016\/j.infsof.2026.108195_b3","doi-asserted-by":"crossref","first-page":"4316","DOI":"10.1109\/TITS.2020.3032227","article-title":"Deep learning for safe autonomous driving: Current challenges and future directions","volume":"22","author":"Muhammad","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.infsof.2026.108195_b4","series-title":"Proceedings of the 35th IEEE\/ACM International Conference on Automated Software Engineering","first-page":"78","article-title":"Enhanced compiler bug isolation via memoized search","author":"Chen","year":"2021"},{"key":"10.1016\/j.infsof.2026.108195_b5","series-title":"Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis","first-page":"169","article-title":"Deepfl: integrating multiple fault diagnosis dimensions for deep fault localization","author":"Li","year":"2019"},{"issue":"10s","key":"10.1016\/j.infsof.2026.108195_b6","doi-asserted-by":"crossref","DOI":"10.1145\/3505243","article-title":"A survey on deep learning for software engineering","volume":"54","author":"Yang","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.infsof.2026.108195_b7","series-title":"Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis","first-page":"427","article-title":"Deep just-in-time defect prediction: how far are we?","author":"Zeng","year":"2021"},{"issue":"3","key":"10.1016\/j.infsof.2026.108195_b8","article-title":"Deep learning and medical diagnosis: A review of literature","volume":"2","author":"Bakator","year":"2018","journal-title":"Multimodal Technol. Interact."},{"issue":"Volume 19, 2017","key":"10.1016\/j.infsof.2026.108195_b9","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","article-title":"Deep learning in medical image analysis","volume":"19","author":"Shen","year":"2017","journal-title":"Annu. Rev. Biomed. Eng."},{"issue":"21","key":"10.1016\/j.infsof.2026.108195_b10","doi-asserted-by":"crossref","DOI":"10.3390\/electronics11213551","article-title":"Deep learning-based pedestrian detection in autonomous vehicles: Substantial issues and challenges","volume":"11","author":"Iftikhar","year":"2022","journal-title":"Electronics"},{"issue":"1","key":"10.1016\/j.infsof.2026.108195_b11","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MSP.2020.2982820","article-title":"Deep neural network perception models and robust autonomous driving systems: Practical solutions for mitigation and improvement","volume":"38","author":"Shafiee","year":"2021","journal-title":"IEEE Signal Process. Mag."},{"key":"10.1016\/j.infsof.2026.108195_b12","series-title":"2019 IEEE\/ACM 41st International Conference on Software Engineering","first-page":"1027","article-title":"CRADLE: Cross-backend validation to detect and localize bugs in deep learning libraries","author":"Pham","year":"2019"},{"key":"10.1016\/j.infsof.2026.108195_b13","series-title":"Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering","first-page":"788","article-title":"Deep learning library testing via effective model generation","author":"Wang","year":"2020"},{"key":"10.1016\/j.infsof.2026.108195_b14","series-title":"Proceedings of the 44th International Conference on Software Engineering","first-page":"1418","article-title":"Muffin: testing deep learning libraries via neural architecture fuzzing","author":"Gu","year":"2022"},{"key":"10.1016\/j.infsof.2026.108195_b15","series-title":"Proceedings of the 44th International Conference on Software Engineering","first-page":"995","article-title":"Free lunch for testing: fuzzing deep-learning libraries from open source","author":"Wei","year":"2022"},{"key":"10.1016\/j.infsof.2026.108195_b16","series-title":"Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis","first-page":"176","article-title":"DocTer: documentation-guided fuzzing for testing deep learning api functions","author":"Xie","year":"2022"},{"key":"10.1016\/j.infsof.2026.108195_b17","series-title":"32nd USENIX Security Symposium (USENIX Security 23)","first-page":"2383","article-title":"IvySyn: Automated vulnerability discovery in deep learning frameworks","author":"Christou","year":"2023"},{"key":"10.1016\/j.infsof.2026.108195_b18","series-title":"Proceedings of the IEEE\/ACM 46th International Conference on Software Engineering","article-title":"Large language models are edge-case generators: Crafting unusual programs for fuzzing deep learning libraries","author":"Deng","year":"2024"},{"key":"10.1016\/j.infsof.2026.108195_b19","series-title":"Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis","first-page":"690","article-title":"Acetest: Automated constraint extraction for testing deep learning operators","author":"Shi","year":"2023"},{"key":"10.1016\/j.infsof.2026.108195_b20","series-title":"2025 IEEE\/ACM 47th International Conference on Software Engineering","first-page":"508","article-title":"Your Fix Is My Exploit: Enabling Comprehensive DL Library API Fuzzing with Large Language Models","author":"Zhang","year":"2025"},{"key":"10.1016\/j.infsof.2026.108195_b21","series-title":"2025 IEEE\/ACM 47th International Conference on Software Engineering","first-page":"2957","article-title":"Lightweight Concolic Testing via Path-Condition Synthesis for Deep Learning Libraries","author":"Kim","year":"2025"},{"key":"10.1016\/j.infsof.2026.108195_b22","series-title":"Evaluating the effectiveness of coverage-guided fuzzing for testing deep learning library APIs","author":"Qin","year":"2025"},{"issue":"11s","key":"10.1016\/j.infsof.2026.108195_b23","doi-asserted-by":"crossref","DOI":"10.1145\/3512345","article-title":"Fuzzing: A survey for roadmap","volume":"54","author":"Zhu","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.infsof.2026.108195_b24","series-title":"14th USENIX Workshop on Offensive Technologies (WOOT 20)","article-title":"AFL++ : Combining incremental steps of fuzzing research","author":"Fioraldi","year":"2020"},{"key":"10.1016\/j.infsof.2026.108195_b25","series-title":"Proceedings of the International Symposium on Code Generation and Optimization: Feedback-Directed and Runtime Optimization","first-page":"75","article-title":"LLVM: A compilation framework for lifelong program analysis & transformation","author":"Lattner","year":"2004"},{"issue":"1","key":"10.1016\/j.infsof.2026.108195_b26","doi-asserted-by":"crossref","DOI":"10.1145\/3688838","article-title":"History-driven fuzzing for deep learning libraries","volume":"34","author":"Shiri Harzevili","year":"2024","journal-title":"ACM Trans. Softw. Eng. Methodol."},{"key":"10.1016\/j.infsof.2026.108195_b27","series-title":"Gcov:a source code coverage analysis and statement-by-statement profiling tool","author":"Collection","year":"2021"},{"key":"10.1016\/j.infsof.2026.108195_b28","series-title":"Robust LLM training infrastructure at ByteDance","author":"Wan","year":"2025"},{"key":"10.1016\/j.infsof.2026.108195_b29","series-title":"Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis","first-page":"423","article-title":"Large language models are zero-shot fuzzers: Fuzzing deep-learning libraries via large language models","author":"Deng","year":"2023"},{"key":"10.1016\/j.infsof.2026.108195_b30","series-title":"Proceedings of the 45th International Conference on Software Engineering","first-page":"1174","article-title":"Fuzzing automatic differentiation in deep-learning libraries","author":"Yang","year":"2023"},{"key":"10.1016\/j.infsof.2026.108195_b31","series-title":"2020 35th IEEE\/ACM International Conference on Automated Software Engineering","first-page":"486","article-title":"Audee: Automated testing for deep learning frameworks","author":"Guo","year":"2020"},{"key":"10.1016\/j.infsof.2026.108195_b32","series-title":"Blocks and fuel: Frameworks for deep learning","author":"van Merri\u00ebnboer","year":"2015"},{"key":"10.1016\/j.infsof.2026.108195_b33","series-title":"Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2","first-page":"530","article-title":"Nnsmith: Generating diverse and valid test cases for deep learning compilers","author":"Liu","year":"2023"},{"key":"10.1016\/j.infsof.2026.108195_b34","series-title":"Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering","first-page":"657","article-title":"Neuri: Diversifying DNN generation via inductive rule inference","author":"Liu","year":"2023"},{"issue":"5","key":"10.1016\/j.infsof.2026.108195_b35","doi-asserted-by":"crossref","DOI":"10.1145\/3583566","article-title":"COMET: Coverage-guided model generation for deep learning library testing","volume":"32","author":"Li","year":"2023","journal-title":"ACM Trans. Softw. Eng. Methodol."},{"key":"10.1016\/j.infsof.2026.108195_b36","series-title":"Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering","first-page":"44","article-title":"Fuzzing deep-learning libraries via automated relational api inference","author":"Deng","year":"2022"},{"key":"10.1016\/j.infsof.2026.108195_b37","series-title":"Proceedings of the 32nd USENIX Conference on Security Symposium","article-title":"Differential testing of cross deep learning framework APIs: revealing inconsistencies and vulnerabilities","author":"Deng","year":"2023"},{"issue":"2","key":"10.1016\/j.infsof.2026.108195_b38","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1109\/JAS.2024.124971","article-title":"When software security meets large language models: A survey","volume":"12","author":"Zhu","year":"2025","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"10.1016\/j.infsof.2026.108195_b39","series-title":"Proceedings of the 45th International Conference on Software Engineering","first-page":"919","article-title":"CodaMosa: Escaping coverage plateaus in test generation with pre-trained large language models","author":"Lemieux","year":"2023"},{"key":"10.1016\/j.infsof.2026.108195_b40","series-title":"Proceedings of the 45th International Conference on Software Engineering","first-page":"1355","article-title":"Fill in the blank: Context-aware automated text input generation for mobile GUI testing","author":"Liu","year":"2023"},{"key":"10.1016\/j.infsof.2026.108195_b41","series-title":"Proceedings of the IEEE\/ACM 47th International Conference on Software Engineering","first-page":"1616","article-title":"The seeds of the FUTURE sprout from history: Fuzzing for unveiling vulnerabilities in prospective deep-learning libraries","author":"Li","year":"2025"}],"container-title":["Information and Software Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950584926001849?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950584926001849?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T11:40:18Z","timestamp":1783165218000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950584926001849"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":41,"alternative-id":["S0950584926001849"],"URL":"https:\/\/doi.org\/10.1016\/j.infsof.2026.108195","relation":{},"ISSN":["0950-5849"],"issn-type":[{"value":"0950-5849","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Effective Python-frontend fuzzing for deep learning libraries with runtime coverage feedback","name":"articletitle","label":"Article Title"},{"value":"Information and Software Technology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.infsof.2026.108195","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"108195"}}