HyProf: A Profiler for Programming Students that Offers Hypotheses about Performance Bugs
Name
3770762.3772564.pdf
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1.27 MB
Format
Adobe PDF
Checksum (MD5)
d1241fdd1d29e71b0ee9ac88e2662572
Author(s)
Dargan, Hope
Hartz, Adam
Miller, Robert
Date Issued
February 17, 2026
Publisher
ACM|Proceedings of the 57th ACM Technical Symposium on Computer Science Education V.1
Citation
Hope 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.
Version
Final published version
Abstract
Programming 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.
Description
SIGCSE TS 2026, St. Louis, MO, USA
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3770762.3772564
