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Wenqing Luo

dblp:277/3692 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2022
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 2 · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
2 papers
Software testing · 47% Program verification · 41% Program analysis · 12%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Cloud and datacenter computing · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computing education · 100%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Software testing › non-functional testing
reliability testing
0.612022
Automatic Reliability Testing For Cluster Management Controllers · OSDI 2022
Cloud and datacenter computing › cluster resource management and scheduling
cluster resource management
0.612022
Automatic Reliability Testing For Cluster Management Controllers · OSDI 2022
Computing education
programming education
0.512021
End-to-End Automation of Feedback on Student Assembly Programs · ASE 2021
Program verification
equivalence checking
0.512021
End-to-End Automation of Feedback on Student Assembly Programs · ASE 2021
Program analysis
static analysis
0.112021
End-to-End Automation of Feedback on Student Assembly Programs · ASE 2021

Methods — techniques the papers use, named apart from their topics

automatic reliability testing · 1.1symbolic execution · 1.0static analysis · 1.0reverse debugging · 1.0KLEE · 1.0
YearPublicationVenuePosition
2022 Automatic Reliability Testing For Cluster Management Controllers
Xudong Sun 0013, Wenqing Luo, Jiawei Tyler Gu, Aishwarya Ganesan, Ramnatthan Alagappan, Michael Gasch, Lalith Suresh 0001, Tianyin Xu
OSDI2
2021 End-to-End Automation of Feedback on Student Assembly Programs
abstract
We developed a set of tools designed to provide rapid feedback to students as they learn to write programs in assembly language (LC-3, a RISC-like educational instruction set architecture). At the heart of the system is an extended version of KLEE, KLC3, that enables us to both identify issues and perform equivalence checking between student code and a gold (correct) version of each assignment. Feedback begins when students edit their code using a VSCode extension that leverages static analysis to perform a variety of correctness and style checks, encouraging students to improve their code quality. Each time a student commits code to their Git repository, our system triggers. Using KLC3 (KLEE), the student code is executed along with the gold version, and issues and behavioral differences are delivered back to the student through their Git repository as a human-readable report, test cases, and scripts. A queueing system allows students to monitor progress, but responses are generally available within minutes. We also extended the LC-3 simulation tools to support reverse debugging, making the process of finding complex bugs much more tractable for students, and used Emscripten to develop a browser-based interface for use in testing and debugging. Finally, our system maintains an individual regression test suite for each student and requires a submission to pass all previous tests before re-evaluation in KLC3, thus avoiding encouraging programming-by-guesswork. We deployed the system to provide feedback for the assembly programming assignments in a class of over 100 students in Fall 2020. Students wrote a median of around 700 lines of assembly for these assignments, making heavy use of our tools to understand and eliminate their bugs. Anonymous student feedback on the tools was uniformly positive. Since that semester, we have continued to refine and expand our tools’ analysis capabilities and performance, and plan to deploy the system again in the near future (the class is offered every Fall).
Zikai Liu, Tingkai Liu, Wenqing Luo, Steven S. Lumetta
ASE4