[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125718-en":3,"doc-seo-125718-105":30,"detail-sidebar-cat-0-en-105":91},{"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},125718,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Test & Evaluation Best Practices for Machine Learning-Enabled Systems","Machine learning-enabled software systems are being adopted across domains, increasing the need for reliable Test and Evaluation (T&E) throughout their lifecycle. This report establishes best practices by structuring the lifecycle into component, integration and deployment, and post-deployment stages. It highlights why T&E is challenging for ML systems, notes gaps in coverage beyond the component level, and explains how inadequate or ad-hoc strategies can erode user confidence. The work calls for systematic testing approaches, adequacy measures, and metrics across all stages.","arXiv :2310 .06800v1 [ cs . SE] 10 Oct 2023  \nTest & Evaluation Best Practices for Machine Learning-Enabled Systems  \nJAGANMOHAN CHANDRASEKARAN, Virginia Tech National Security Institute, USA  \nTYLER CODY, Virginia Tech National Security Institute, USA NICOLA MCCARTHY, Virginia Tech National Security Institute, USA ERIN LANUS, Virginia Tech National Security Institute, USA LAURA FREEMAN, Virginia Tech National Security Institute, USA  \nMachine learning (ML)– based software systems are rapidly gaining adoption across various domains, making it increasingly essential to ensure they perform as intended. This report presents best practices for the Test and Evaluation (T&E) of ML-enabled software systems across its lifecycle. We categorize the lifecycle of ML-enabled software systems into three stages: component, integration and deployment, and post-deployment. At the component level, the primary objective is to test and evaluate the ML model as a standalone component. Next, in the integration and deployment stage, the goal is to evaluate an integrated ML-enabled system consisting of both ML and non-ML components. Finally, once the ML-enabled software system is deployed and operationalized, the T&E objective is to ensure the system performs as intended. Maintenance activities for ML-enabled software systems span the lifecycle and involve maintaining various assets of ML-enabled software systems.  \nGiven its unique characteristics, the T&E of ML-enabled software systems is challenging. While significant research has been reported on T&E at the component level, limited work is reported on T&E in the remaining two stages. Furthermore, in many cases, there is a lack of systematic T&E strategies throughout the ML-enabled system’s lifecycle. This leads practitioners to resort to ad-hoc T&E practices, which can undermine user confidence in the reliability of ML-enabled software systems. New systematic testing approaches, adequacy measurements, and metrics are required to address the T&E challenges across all stages of the ML-enabled system lifecycle.  \nAdditional Key Words and Phrases: Test and Evaluation, Best Practices, Machine Learning, Testing ML, Test generation, Test Adequacy, Model Deployment, ML re-engineering  \n1 INTRODUCTION  \nML has made significant strides in the past decade, leading to its adoption across various domains. ML includes methods spanning from classic statistical modeling approaches like linear models and decision trees to modern modeling approaches like deep learning. With ML, problems that were once too complex or impossible to handle and beyond the capabilities of traditional software systems can now be addressed. Thus, ML-enabled software systems are becoming increasingly prevalent. As more and more organizations across domains pivot towards leveraging ML-based solutions to address their needs, ensuring that ML-enabled software systems work as expected becomes increasingly important.  \nThe lifecycle of an ML-enabled software system, as presented in Figure 1, is a multi-stage process that involves scoping, data collection and processing, ML model development, integration, deployment, and post-deployment activities. The lifecycle commences with scoping, followed by data collection and processing. Once the data is prepared, practitioners begin the ML model development process. In this phase, the practitioner selects an ML algorithm from an ML framework, which is a collection of libraries and tools for building ML models. The ML algorithm, on receiving data and hyperparameters as inputs, analyzes and learns from the data in an iterative manner. The learned logic is referred  \nAuthors’ addresses: Jaganmohan Chandrasekaran, Virginia Tech National Security Institute, Arlington, USA, [jagan@vt.edu](jagan@vt.edu); Tyler Cody, Virginia Tech National Security Institute, Arlington, USA, [tcody@vt.edu](tcody@vt.edu); Nicola McCarthy, Virginia Tech National Security Institute, Arlington, USA, nicmccarthy@vt. edu; Erin Lanus","cbCaiq9BMelBvsNu","https://ap.wps.com/l/cbCaiq9BMelBvsNu","pdf",642599,1,24,"English","en",105,"# Introduction\n## Lifecycle of ML-enabled software systems\n## Differences between traditional T&E and ML-based systems\n## Challenges and need for systematic practices","[{\"question\":\"How does the report structure the lifecycle of ML-enabled software systems for T\\u0026E purposes?\",\"answer\":\"It divides the lifecycle into three stages: component, integration and deployment, and post-deployment, aligning the T\\u0026E objectives to each stage.\"},{\"question\":\"What is the main T\\u0026E objective at the component level?\",\"answer\":\"The objective is to test and evaluate the ML model as a standalone component.\"},{\"question\":\"Why are traditional testing and evaluation approaches insufficient for ML-enabled systems?\",\"answer\":\"ML systems derive decision logic from datasets using algorithms, resulting in behavior that is harder to interpret for humans; this black-box nature makes traditional T\\u0026E approaches inadequate.\"}]","Test & Evaluation Best Practices for Machine Learning-Enabled Systems | PDF",1785900820,60,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"test-evaluation-best-practices-for-machine-learning-enabled-systems","",{"@graph":36,"@context":85},[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/test-evaluation-best-practices-for-machine-learning-enabled-systems/125718/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the report structure the lifecycle of ML-enabled software systems for T&E purposes?","Question",{"text":75,"@type":76},"It divides the lifecycle into three stages: component, integration and deployment, and post-deployment, aligning the T&E objectives to each stage.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the main T&E objective at the component level?",{"text":80,"@type":76},"The objective is to test and evaluate the ML model as a standalone component.",{"name":82,"@type":73,"acceptedAnswer":83},"Why are traditional testing and evaluation approaches insufficient for ML-enabled systems?",{"text":84,"@type":76},"ML systems derive decision logic from datasets using algorithms, resulting in behavior that is harder to interpret for humans; 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