Unsupervised Voting for Detecting the Algorithmic Solving Strategy in Competitive Programming Solutions

Unsupervised Voting for Detecting the Algorithmic Solving Strategy in Competitive Programming Solutions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Unsupervised Voting for Detecting the Algorithmic Solving Strategy in Competitive Programming Solutions Alexandru Stefan Stoica, Daniel Băbiceanu, Traian Rebedea, Marian Cristian Mihăescu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2834777/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The problem of source code analysis using machine learning techniques has been gaining much attention recently as several powerful code embeddings methods have been created. Having available different embedding methods for source code opened the way for tackling many practical problems related to source code analysis. This paper addresses the issue of determining the number of distinct algorithmic strategies that may be found in a set of correct solutions for a competitive programming problem. We have investigated using five embedding methods with three algorithmic strategies in a data analysis pipeline that tackle the previously described issues on a newly created dataset consisting of 15 algorithmic problems. We propose an unsupervised algorithm that considers each embedding as a different dataset view. On each view, a hard clustering algorithm can be employed to split the correct solutions into K distinct algorithmic approaches, where K is a hyperparameter. In the ideal case, if all the algorithmic approaches in each view are determined correctly, a mapping could be done between each consecutive view such that each algorithmic approach from one view maps to a single corresponding algorithmic approach in the other. We are interested in determining the maximum subset of solutions that satisfy the assumption from above and use this subset as a base for a co-training procedure where an estimator employed on each view votes for the algorithmic approach of each solution which is not part of the subset. According to the results, the proposed unsupervised voting algorithm highly improves the baseline clustering approach. This improvement was observed across all problems in the dataset except one. Scale-up of the data analysis pipeline to datasets of thousands of problems may yield the ability to profoundly understand and learn about the innovative process of correctly designing and writing code in the context of competitive programming or even industry code. source code competitive programming machine learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2834777","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":193169285,"identity":"41f45555-6d43-47f3-b9c6-0a63f014a5e5","order_by":0,"name":"Alexandru Stefan Stoica","email":"","orcid":"","institution":"University Politechnica of Bucharest","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Alexandru","middleName":"Stefan","lastName":"Stoica","suffix":""},{"id":193169289,"identity":"bdc83819-fdc2-45a6-ab7b-850444af99df","order_by":1,"name":"Daniel Băbiceanu","email":"","orcid":"","institution":"University of Craiova","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Băbiceanu","suffix":""},{"id":193169290,"identity":"6c5c3a97-5aa0-47bd-800d-417769709808","order_by":2,"name":"Traian Rebedea","email":"","orcid":"","institution":"University Politechnica of Bucharest","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Traian","middleName":"","lastName":"Rebedea","suffix":""},{"id":193169291,"identity":"80f12df1-e61d-4489-bc16-54b7bb2472c0","order_by":3,"name":"Marian Cristian Mihăescu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABGUlEQVRIiWNgGAWjYFACxgYGBgMwow0iwN7AIEFQywEULTwHCGkBggMQig1CSSTg18I/+3Dz5w8FdnkMYofbHvyoqJM3uPn44G0ehsPyBgd4Hz7AokXiXGKbxAGD5GIG6cR2w54zhw033E5LtgZqMdxwgN3YAIsWAx6gFw4YMCc2SAP18rYdSDC4nWMmDdSSINnAxobNhUAtzR8OGNSDtUj+/VeXYHDz/DdCWhqADjsM1iLN28CcYHCDhw2shZ8BuxaJM4xtEmcMjgPVA5HMscOGM8+kGVvOMUg37GdmY8bmF/4e9scfKv5UJ/ZLpz+TfFNTJ893/PDDG28qrOXZ2NsYsYUYHLChORiImfGpHwWjYBSMglGADwAAIaVcFzNntxIAAAAASUVORK5CYII=","orcid":"","institution":"University Politechnica of Bucharest","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Marian","middleName":"Cristian","lastName":"Mihăescu","suffix":""}],"badges":[],"createdAt":"2023-04-19 05:29:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2834777/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2834777/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":42886720,"identity":"b701b986-c485-4fc3-8063-87966f64d273","added_by":"auto","created_at":"2023-09-09 22:22:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":351417,"visible":true,"origin":"","legend":"","description":"","filename":"CodeAnalysisKAISJournalsubmitted19.04.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2834777/v1_covered_66e11000-879a-43df-9cd8-feee9b4af9c6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unsupervised Voting for Detecting the Algorithmic Solving Strategy in Competitive Programming Solutions","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"source code, competitive programming, machine learning","lastPublishedDoi":"10.21203/rs.3.rs-2834777/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2834777/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe problem of source code analysis using machine learning techniques has been gaining much attention recently as several powerful code embeddings methods have been created. Having available different embedding methods for source code opened the way for tackling many practical problems related to source code analysis. This paper addresses the issue of determining the number of distinct algorithmic strategies that may be found in a set of correct solutions for a competitive programming problem. We have investigated using five embedding methods with three algorithmic strategies in a data analysis pipeline that tackle the previously described issues on a newly created dataset consisting of 15 algorithmic problems. We propose an unsupervised algorithm that considers each embedding as a different dataset view. On each view, a hard clustering algorithm can be employed to split the correct solutions into K distinct algorithmic approaches, where K is a hyperparameter. 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