One of the biggest challenges in building software is ensuring that what you build is correct. This year, at ICSE 2026, UMass LASER has two papers that bring us closer to that vision, using AI to automatically prove correctness.
cics.umass.edu/news/fse-test…
Incredibly honored to receive the ACM International Conference on the Foundations of Software Engineering 2025 Test of Time Award honorable mention for our work on overfitting in automated program repair. with Ted, @clegoues, & Earl.
The work identified what can go wrong when automated tools repair bugs and created an objective methodology for measuring patch quality used widely in modern repair research.
Original paper: doi.org/10.1145/2786805.2786…
LLMs offer an amazing opportunity to automate formal verification because the theorem prover is an oracle, identifying hallucinations and wrong proofs.
Hear about our Distinguished Paper Award work at @icseconf.bsky.social at 12:15 PM on Wed, room 212.
conf.researchr.org/details/i…
Come learn how reinforcement learning can significantly improve automatic proof synthesis for formal verification!
Hear our talk on QEDCartographer at ICSE 2025 at 11:30 AM on Wednesday in room 212.
conf.researchr.org/details/i…
Paper: people.cs.umass.edu/~brun/pu…
Are you graduating with a PhD? Do you work on program analysis, formal verification, software correctness, or AI? Apply to join the UMass LASER lab as a postdoctoral researcher, a vibrant team focused on using the latest NLP technology to ensure software correctness!
I am soliciting applications for a postdoctoral fellowship. If interested, please submit a CV, the names of 3 reference letter writers, and a short (~1 paragraph) statement of interest and a potential starting date by MARCH 15, 2025.
Contact Yuriy Brun <brun@cs.umass.edu> for questions and to discuss the position. See some of our latest work for examples of high-impact use of NLP for software correctness. people.cs.umass.edu/~brun/pu… (ICSE'25) people.cs.umass.edu/~brun/pu… (ICSE'25)
Zhanna Kaufman presenting our VIS'23 paper on how ML bias affects people's trust.
Paper: My Model is Unfair, Do People Even Care? Visual Design Affects Trust and Perceived Bias in Machine Learning
people.cs.umass.edu/~brun/pu…@thecindyxiong , @aimen_gaba@manningcics
Our paper aims to understand how bias and effectiveness of data-driven systems affect trust in systems. In a study with over 1,500 users, we find that (1) women weight bias more than men do when deciding whom to trust, regardless of whether the bias favors men or women,
(2) describing system behavior using text rather than bar charts leads to people putting more weight on bias, and (3) explicitly labeling a system as biased has more effect than showing a history of biased behavior.