Yuriy Brun

50 posts

Yuriy Brun

Yuriy Brun

@YuriyBrun

see https://t.co/1q9tzWkQ2y Professor at the University of Massachusetts Amherst. https://t.co/QNezwkmbD0

Katılım Ekim 2019
0 Takip Edilen183 Takipçiler
Yuriy Brun
Yuriy Brun@YuriyBrun·
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/10.1145/278680…
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Yuriy Brun
Yuriy Brun@YuriyBrun·
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.
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Yuriy Brun
Yuriy Brun@YuriyBrun·
Claire Le Goues @clegoues gave a fantastic ISSTA @issta_conf 2025 keynote this morning on software correctness in the age of generative AI.
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Yuriy Brun
Yuriy Brun@YuriyBrun·
Congrats to all the LASER lab award winners at @ICSEconf 2025!
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Yuriy Brun
Yuriy Brun@YuriyBrun·
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/icse-2…
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Yuriy Brun
Yuriy Brun@YuriyBrun·
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.
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Yuriy Brun
Yuriy Brun@YuriyBrun·
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!
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Yuriy Brun
Yuriy Brun@YuriyBrun·
@thecindyxiong @aimen_gaba @manningcics (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.
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Yuriy Brun
Yuriy Brun@YuriyBrun·
@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,
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