[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81965-en":3,"doc-seo-81965-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},81965,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Deployment Risk Assessment Using Diff-Aware Features: A Case Study at Prime Video","At Amazon Prime Video, code deployments during live events and rapid feature releases must avoid service outages, yet blanket deployment freezes block all changes and increase developer toil. Prior risky change predictors depend on developer-specific metadata or extensive history, raising privacy concerns and limiting reuse on new projects. This work introduces a diff-aware framework using code-modification-derived characteristics, including quantifiable metrics and qualitative indicators such as style violations and change types, extracted by LLMs for code analysis. Evaluations on Prime Video and ApacheJIT achieve strong recall (0.83) and F1 (0.81).","Deployment Risk Assessment Using Diff-Aware Features: A Case  \nStudy at Prime Video  \nMayur Kurup∗ [Amazon.com](Amazon.com)[ ](Amazon.com)Austin, Texas, USA  \nPranesh Vyas  \n[Amazon.com](Amazon.com)[ ](Amazon.com)Austin, Texas, USA  \nHyunjae Suh∗† University of California Irvine, California, USA  \nSrinidhi Madabhushi  \n[Amazon.com](Amazon.com)[ ](Amazon.com)Seattle, Washington, USA  \nSwathi Vaidyanathan  \n[Amazon.com](Amazon.com)[ ](Amazon.com)Seattle, Washington, USA  \nYegor Silyutin  \n[Amazon.com](Amazon.com)[ ](Amazon.com)Seattle, Washington, USA  \narXiv :2607 .06766v2 [ cs . SE] 10 Jul 2026  \nAbstract  \nAt Amazon Prime Video, we face the critical operational challenge of managing code deployments during live events and rapid feature releases without causing service outages. Current change control approaches use blanket deployment freezes that block all changes regardless of risk, creating significant developer toil. While prior research has explored risky change predictors, these rely on developerspecific metadata or extensive historical data, raising privacy concerns and limiting applicability to new projects. We introduce a framework centered on diff-aware features—characteristics derived directly from code modifications. Our key contribution is the systematic identification of which quantitative metrics (code-level and change-level metrics) and qualitative indicators (coding style violations, change type classification) are necessary for risk prediction. We employ LLMs as multi-language feature extractors, demonstrating their effectiveness for code analysis beyond generation tasks and eliminating the need for language-specific tooling. We evaluated our framework on two datasets: Prime Video’s production environment and the public ApacheJIT dataset. Our best-performing model achieves an average recall of 0.83 and F1 score of 0.81 across both datasets for detecting risky code changes. Notably, ablation analysis reveals that change-level volume metrics (e.g., lines added/deleted) are noisy predictors, while structural code complexity provides a substantially stronger risk signal. These results demonstrate that thoughtful feature curation enables effective change risk assessment across different programming languages and organizational contexts while avoiding privacy concerns.  \nCCS Concepts  \n• Software and its engineering → Software testing and debugging; Software maintenance tools.  \nKeywords  \nChange Control, Code Freeze, Change Risk, Proactive Safety, Justin-Time Defect Prediction  \n1 Introduction  \nLive event streaming services such as Prime Video operate under stringent reliability requirements, particularly during large-scale broadcasts like NFL Thursday Night Football (TNF) and Premier  \n∗ Both authors contributed equally to this work †Work done during Amazon internship  \nLeague matches, where millions of viewers depend on uninterrupted service. Even a single faulty code change can cause widespread disruptions that are difficult to mitigate during events. To reduce this risk, organizations commonly employ broad deployment freezes during high-traffic windows. While these freezes prevent failures, they delay feature rollouts, create pending change accumulations, and require special handling for urgent updates. As the number and complexity of live events continue to grow, the operational cost of such freezes increases correspondingly, making more precise assessment of individual code-change risk increasingly important.  \nThe central challenge is determining which changes warrant caution without relying on coarse, event-long freezes. Although prior research on change-and defect-prediction provides useful insights [8, 14], many approaches depend on developer-specific metadata or substantial historical project information. These dependencies raise privacy considerations, hinder adoption across diverse teams, and limit applicability to new or rapidly evolving codebases. Other models demonstrate effectiveness only within a","cbCaip6j2LwXmAWp","https://ap.wps.com/l/cbCaip6j2LwXmAWp","pdf",779030,3,1,6,"English","en",105,"# Introduction\n## Motivation and challenges\n## Diff-aware approach and guardrail deployment model\n# Method and evaluation\n## Feature identification and extraction\n## Datasets and results\n# Analysis\n## Ablation study and key insights","[{\"question\":\"Why do Prime Video and similar teams use deployment freezes during live events?\",\"answer\":\"They use broad freezes to prevent faulty code changes from causing widespread disruptions during high-traffic broadcasts. However, freezes also delay feature rollouts and create operational overhead for urgent updates.\"},{\"question\":\"What key limitation do existing change-risk prediction methods have?\",\"answer\":\"Many approaches rely on developer-specific metadata or large historical datasets, which raises privacy concerns and makes results harder to apply to new or rapidly changing projects.\"},{\"question\":\"How does the proposed framework predict risky changes?\",\"answer\":\"It predicts risk using diff-aware features derived directly from code modifications, combining quantitative code-level/change-level metrics with qualitative indicators such as coding style violations and change type classification. LLMs act as multi-language feature extractors for code analysis.\"}]","Deployment Risk Assessment Using Diff-Aware Features: A Case Study at Prime Video | PDF",1784177329,15,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"deployment-risk-assessment-using-diff-aware-features-a-case-study-at-prime-video","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/deployment-risk-assessment-using-diff-aware-features-a-case-study-at-prime-video/81965/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-07-29","2026-07-16",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why do Prime Video and similar teams use deployment freezes during live events?","Question",{"text":76,"@type":77},"They use broad freezes to prevent faulty code changes from causing widespread disruptions during high-traffic broadcasts. However, freezes also delay feature rollouts and create operational overhead for urgent updates.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What key limitation do existing change-risk prediction methods have?",{"text":81,"@type":77},"Many approaches rely on developer-specific metadata or large historical datasets, which raises privacy concerns and makes results harder to apply to new or rapidly changing projects.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the proposed framework predict risky changes?",{"text":85,"@type":77},"It predicts risk using diff-aware features derived directly from code modifications, combining quantitative code-level/change-level metrics with qualitative indicators such as coding style violations and change type classification. 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