Jack Wrenn

TL;DR:

Ph.D. student in Computer Science at Brown University.

Stuff I Work On

Pyret
A programming language designed by computer science educators, for computer science education.
Code.Pyret.Org
The best way to write Pyret programs.
Liber Brunoniana
Using NLP to breath new life into old encyclopedias.
Fireplace
A no-nonsense command-line graphing utility written in Rust.
Flake
An infinite whiteboard for stylus+touch devices written in Rust.
tfw
Shell utility for conditional redirection.
bam
Use the PC motherboard speaker as a monophonic MIDI synthesizer.
midiplex
Volume-aware splitting of a polyphonic MIDI stream into multiple, monophonic streams.
midinet
Fire MIDI events across the network.

Publications

Error Messages Are Classifiers
Co-Authors
Shriram Krishnamurthi
Downloads
Paper Talk
Abstract
We take the perspective that error reports are really classifiers of program information. They should therefore be subjected to the same measures as other classifiers (e.g., precision and recall). We formalize this perspective as a process for assessing error reports, describe our application of this process to Pyret, and present a preliminary study on the utility of the resulting error reports.
DOI
10.1145/3133850.3133862
Who Tests the Testers?
Co-Authors
Shriram Krishnamurthi, Kathi Fisler
Downloads
Paper
Abstract
Instructors routinely use automated assessment methods to evaluate the semantic qualities of student implementations and, sometimes, test suites. In this work, we distill a variety of automated assessment methods in the literature down to a pair of assessment models. We identify pathological assessment outcomes in each model that point to underlying methodological flaws. These theoretical flaws broadly threaten the validity of the techniques, and we actually observe them in multiple assignments of an introductory programming course. We propose adjustments that remedy these flaws and then demonstrate, on these same assignments, that our interventions improve the accuracy of assessment. We believe that with these adjustments, instructors can greatly improve the accuracy of automated assessment.
DOI
10.1145/3230977.3230999

Me, Elsewhere