[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121309-en":3,"doc-seo-121309-105":30,"detail-sidebar-cat-0-en-105":83},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121309,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Studying the Impact of TensorFlow and PyTorch Bindings on Machine Learning Software Quality","This paper studies how using TensorFlow and PyTorch bindings affects machine learning software quality across multiple non-default programming languages, focusing on correctness and time cost. Experiments train and run inference on five widely used deep learning models, measuring training and test accuracy as correctness and tracking training and inference latency. Results show that a model can be trained with one binding and inferred with another binding without losing accuracy. The findings indicate non-default bindings can improve time cost while maintaining the same correctness level.","arXiv :2407 .05466v 1 [ cs . SE] 7 Jul 2024  \nStudying the Impact of TensorFlow and PyTorch Bindings on Machine Learning Software Quality  \nHAO LI∗ , University of Alberta, Canada  \nGOPI KRISHNAN RAJBAHADUR, Centre for Software Excellence, Huawei Canada, Canada COR-PAUL BEZEMER∗ , University of Alberta, Canada  \nBindings for machine learning frameworks (such as TensorFlow and PyTorch) allow developers to integrate a framework’s functionality using a programming language different from the framework’s default language (usually Python) . In this paper, we study the impact of using TensorFlow and PyTorch bindings in C\\#, Rust, Python and JavaScript on the software quality in terms of correctness (training and test accuracy) and time cost (training and inference time) when training and performing inference on five widely used deep learning models. Our experiments show that a model can be trained in one binding and used for inference in another binding for the same framework without losing accuracy. Our study is the first to show that using a non-default binding can help improve machine learning software quality from the time cost perspective compared to the default Python binding while still achieving the same level of correctness.  \nCCS Concepts: • Software and its engineering → Software libraries and repositories; Software performance; Correctness; • Computing methodologies → Neural networks.  \nAdditional KeyWords and Phrases: Software engineering for machine learning, Software quality, Deep learning, Binding, TensorFlow, PyTorch  \nACM Reference Format:  \nHao Li, Gopi Krishnan Rajbahadur, and Cor-Paul Bezemer. 2024. Studying the Impact of TensorFlow and PyTorch Bindings on Machine Learning Software Quality. ACM Trans. Softw. Eng. Methodol. , ( 2024), 31 pages.  \n[https://doi.org/10.1145/nnnnnnn.nnnnnnn](https://doi.org/10.1145/nnnnnnn.nnnnnnn)  \n1 INTRODUCTION  \nThe rapidly improving capabilities of Deep Learning (DL) and Machine Learning (ML) frameworkshave been the main drivers that allow new intelligent software applications, such as self-driving cars [27, 61] and robotic surgeons [18, 77, 82] . These intelligent software systems all contain components that integrate one or more complex DL and/or ML algorithms. Fortunately, over the past decade, the need for coding these ML and DL algorithms from scratch has been largely eliminated by the availability of several mature ML frameworks and tools such as TensorFlow [1] and PyTorch [63] . These frameworks provide developers with a high-level interface to integrate ML functionality into their projects. Using such ML frameworks has several advantages including  \n∗ Hao Li and Cor-Paul Bezemer are with the Analytics of Software, GAmes And Repository Data (ASGAARD) Lab, University of Alberta, Canada.  \nAuthors’ addresses: Hao Li, [li.hao@ualberta.ca](li.hao@ualberta.ca), University of Alberta, Edmonton, AB, Canada, T6G 2R3; Gopi Krishnan Rajbahadur, Centre for Software Excellence, Huawei Canada, Kingston, ON, Canada, K7L 1H3, gopi.krishnan.rajbahadur1@ [huawei.com](huawei.com); Cor-Paul Bezemer, [bezemer@ualberta.ca](bezemer@ualberta.ca), University of Alberta, Edmonton, AB, Canada, T6G 2R3.  \nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires [prior specific permission and/or a fee. Request permissions from permissions@acm.org](prior specific permission and/or a fee. Request permissions from permissions@acm.org).  \n© 2024 Association for Computing Machinery.  \n1049-331X/2024/0-ART $15.00  \n[https://doi.org/10.1145/nnnnnnn.nnnnnnn](https://doi.org/10.1145","cbCaiaRPOcoLXCm1","https://ap.wps.com/l/cbCaiaRPOcoLXCm1","pdf",981339,1,31,"English","en",105,"# Introduction\n## ML frameworks and default Python access\n## Bindings for non-Python developers\n## Research motivation and prior work","[{\"question\":\"What is the main benefit of non-default bindings reported by the study?\",\"answer\":\"Non-default bindings can improve time cost compared with the default Python binding while still achieving the same correctness level.\"}]","Studying the Impact of TensorFlow and PyTorch Bindings on Machine Learning Software Quality | PDF",1785734998,78,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":78,"head_meta":80,"extra_data":82,"updated_unix":28},"studying-the-impact-of-tensorflow-and-pytorch-bindings-on-machine-learning-software-quality","",{"@graph":36,"@context":77},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/studying-the-impact-of-tensorflow-and-pytorch-bindings-on-machine-learning-software-quality/121309/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main benefit of non-default bindings reported by the study?","Question",{"text":75,"@type":76},"Non-default bindings can improve time cost compared with the default Python binding while still achieving the same correctness level.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,120,123,127],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":98,"slug":130},19,"General","general"]