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squaresLab

Software QUAlity in Real Evolving Systems*

We research automated techniques to reason about, assure, measure, and improve the quality of real, messy, ever-changing software.

Carnegie Mellon University, School of Computer Science

squaresLab on GitHub  ·  clegoues@cs.cmu.edu

*We apologize, sort of, for the egregious backronym.

Team

Claire Le Goues Claire
Andrea Miller Andrea
Cláudia Mamede Cláudia
Harrison Green Harrison
Kaia Newman Kaia
Luke Dramko Luke
+2 more →
View full team →

Recent Publications

Proceedings of the 48th IEEE/ACM International Conference on Software Engineering (ICSE)

FrameShift: Resizing Fuzzer Inputs Without Breaking Them

Harrison Green, Claire Le Goues, Fraser Brown

ACM Trans. Softw. Eng. Methodol.

When Fine-Tuning LLMs Meets Data Privacy: An Empirical Study of Federated Learning in LLM-Based Program Repair

Wenqiang Luo, Jacky W. Keung, Boyang Yang, He Ye, Claire Le Goues, Tegawendé F. Bissyandé, Haoye Tian, Bach Le

ACM Trans. Softw. Eng. Methodol.

MORepair: Teaching LLMs to Repair Code via Multi-Objective Fine-Tuning

Boyang Yang, Haoye Tian, Jiadong Ren, Hongyu Zhang, Jacques Klein, Tegawendé F. Bissyandé, Claire Le Goues, Shunfu Jin

Proceedings of the Network and Distributed System Security Symposium (NDSS)

Idioms: A Simple and Effective Framework for Turbo-Charging Local Neural Decompilation with Well-Defined Types

Luke Dramko, Claire Le Goues, Edward J. Schwartz

Proceedings of the 40th IEEE/ACM International Conference on Automated Software Engineering (ASE)

Interpretable Vulnerability Detection Reports

Cláudia Mamede, José Campos, Claire Le Goues, Rui Abreu

View all 120 publications →

Highlights

2026-04
FrameShift: Resizing Fuzzer Inputs Without Breaking Them

Proceedings of the 48th IEEE/ACM International Conference on Software Engineering (ICSE) 2026

2026-01
Idioms: A Simple and Effective Framework for Turbo-Charging Local Neural Decompilation with Well-Defined Types

Proceedings of the Network and Distributed System Security Symposium (NDSS) 2026

2025-01
Interpretable Vulnerability Detection Reports

Proceedings of the 40th IEEE/ACM International Conference on Automated Software Engineering (ASE) 2025