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dc.contributor.authorDargan, Hope
dc.contributor.authorHartz, Adam
dc.contributor.authorMiller, Robert
dc.date.accessioned2026-03-04T17:11:45Z
dc.date.available2026-03-04T17:11:45Z
dc.date.issued2026-02-17
dc.identifier.isbn979-8-4007-2256-1
dc.identifier.urihttps://hdl.handle.net/1721.1/165015
dc.descriptionSIGCSE TS 2026, St. Louis, MO, USAen_US
dc.description.abstractProgramming students often struggle to find and fix performance bugs in their code. To provide students additional performance debugging support, as well as expose them to profiling tools, we developed Hypothesis Profiler (HyProf). HyProf automatically profiles a slow student submission and produces a profile visualization suitable for learners. In addition to showing individual function and line times, HyProf shows details about the call graph, lines that made recursive calls or did not execute, and hypotheses about possible causes of slow performance, formulated by comparing the slow profile against fast submissions from other students. We deployed HyProf in a 400-student Python course and evaluated it through web logs, office hour observations, and surveys, which showed that 75% of respondents successfully used HyProf to find or fix a performance issue and 85% would recommend it to others.en_US
dc.publisherACM|Proceedings of the 57th ACM Technical Symposium on Computer Science Education V.1en_US
dc.relation.isversionofhttps://doi.org/10.1145/3770762.3772564en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceAssociation for Computing Machineryen_US
dc.titleHyProf: A Profiler for Programming Students that Offers Hypotheses about Performance Bugsen_US
dc.typeArticleen_US
dc.identifier.citationHope Dargan, Adam J. Hartz, and Robert C. Miller. 2026. HyProf: A Profiler for Programming Students that Offers Hypotheses about Performance Bugs. In Proceedings of the 57th ACM Technical Symposium on Computer Science Education V.1 (SIGCSE TS 2026), Vol. 1. Association for Computing Machinery, New York, NY, USA, 253–259.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.identifier.mitlicensePUBLISHER_CC
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2026-03-01T08:46:18Z
dc.language.rfc3066en
dc.rights.holderThe author(s)
dspace.date.submission2026-03-01T08:46:18Z
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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