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Automating Programming Assignment Marking with AST Analysis

dc.contributor.authorLi, Sichuang
dc.date.accessioned2019-01-14T16:04:18Z
dc.date.available2019-01-14T16:04:18Z
dc.date.issued2019-01-14
dc.date.submitted2019-01-08
dc.description.abstractThis thesis presents a novel approach to automatically mark programming assignments. We hypothesize that correct student solution ASTs will be more similar to reference solution ASTs than incorrect student solutions and that their similarities can be quantitatively measured. Our approach first preprocesses the ASTs before computing their tree edit distances. We then aggregate the student's set of edit distances from every reference solution into a final mark for the student. We have implemented our approach in our ClangAutoMarker tool. Our experiments demonstrate promising potential for reducing a human marker's workload but further refinements are needed before its accuracy can be suitable for a live classroom.en
dc.identifier.urihttp://hdl.handle.net/10012/14348
dc.language.isoenen
dc.pendingfalse
dc.publisherUniversity of Waterlooen
dc.subjectassignmenten
dc.subjectclusteringen
dc.subjectmachine learningen
dc.subjectast analysisen
dc.subjectcompileren
dc.subjectclangen
dc.subjectllvmen
dc.titleAutomating Programming Assignment Marking with AST Analysisen
dc.typeMaster Thesisen
uws-etd.degreeMaster of Mathematicsen
uws-etd.degree.departmentDavid R. Cheriton School of Computer Scienceen
uws-etd.degree.disciplineComputer Scienceen
uws-etd.degree.grantorUniversity of Waterlooen
uws.contributor.advisorLam, Patrick
uws.contributor.affiliation1Faculty of Mathematicsen
uws.peerReviewStatusUnrevieweden
uws.published.cityWaterlooen
uws.published.countryCanadaen
uws.published.provinceOntarioen
uws.scholarLevelGraduateen
uws.typeOfResourceTexten

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