Research Seminars @ Illinois

View Full Calendar

Tailored for undergraduate researchers, this calendar is a curated list of research seminars at the University of Illinois. Explore the diverse world of research and expand your knowledge through engaging sessions designed to inspire and enlighten.

To have your events added or removed from this calendar, please contact OUR at ugresearch@illinois.edu

FM/SE seminar Series

Event Type
Seminar/Symposium
Sponsor
PL/FM/SE
Location
0222 Siebel Center and Zoom
Virtual
wifi event
Date
Nov 15, 2024   2:00 - 3:00 pm  
Speaker
Yang Chen, UIUC and Priyam Sahoo, UIUC
Contact
Kristin Irle
E-Mail
kirle@illinois.edu
Phone
217-244-0229
Views
9
Originating Calendar
Siebel School Speakers Calendar
Talk 1 (2-2.30):
Title: Neurosymbolic Repair of Test Flakiness
Speaker: Yang Chen, UIUC

Abstract: Test flakiness, a non-deterministic behavior of builds irrelevant to code changes, is a major and continuing impediment to delivering reliable software. The very few techniques for the automated repair of test flakiness are specifically crafted to repair either Order- Dependent (OD) or Implementation-Dependent (ID) flakiness. They are also all symbolic approaches, i.e., they leverage program analy- sis to detect and repair known test flakiness patterns and root causes, failing to generalize. To bridge the gap, we propose FlakyDoctor, a neuro-symbolic technique that combines the power of LLMs— generalizability—and program analysis—soundness—to fix different types of test flakiness. Our extensive evaluation using 873 confirmed flaky tests (332 OD and 541 ID) from 243 real-world projects demonstrates the ability of FlakyDoctor in repairing flakiness, achieving 57% (OD) and 59% (ID) success rate. Comparing to three alternative flakiness repair approaches, FlakyDoctor can repair 8% more ID tests than DexFix, 12% more OD flaky tests than ODRepair, and 17% more OD flaky tests than iFixFlakies. Regardless of underlying LLM, the non-LLM components of FlakyDoctor contribute to 12–31 % of the overall performance, i.e., while part of the FlakyDoctor power is from using LLMs, they are not good enough to repair flaky tests in real-world projects alone. What makes the proposed technique superior to related research on test flakiness mitigation specifically and program repair, in general, is repairing 79 previously unfixed flaky tests in real-world projects. We opened pull requests for all cases with corresponding patches; 19 of them were accepted and merged at the time of submission.

Talk 2 (2.30-3):
Title: Solving olympiad geometry without human demonstrations
Speaker: Priyam Sahoo, UIUC
Abstract: Proving mathematical theorems at the olympiad level represents a notable milestone in human-level automated reasoning, owing to their reputed difficulty among the world’s best talents in pre-university mathematics. Current machine-learning approaches, however, are not applicable to most mathematical domains owing to the high cost of translating human proofs into the machine-verifiable format. The problem is even worse for geometry because of its unique translation challenges, resulting in a severe scarcity of training data. The authors propose AlphaGeometry, a theorem prover for Euclidean plane geometry that sidesteps the need for human demonstrations by synthesizing millions of theorems and proofs across different levels of complexity. AlphaGeometry is a neuro-symbolic system that uses a neural language model, trained from scratch on our large-scale synthetic data, to guide a symbolic deduction engine through infinite branching points in challenging problems. On a test set of 30 latest olympiad-level problems, AlphaGeometry solves 25, outperforming the previous best method that only solves ten problems and approaching the performance of an average International Mathematical Olympiad (IMO) gold medallist. Notably, AlphaGeometry produces human-readable proofs, solves all geometry problems in the IMO 2000 and 2015 under human expert evaluation, and discovers a generalized version of a translated IMO theorem in 2004.
link for robots only