InteNSE 2024 Contributors - InteNSE 2024

Fri 12 - Sun 21 April 2024 Lisbon, Portugal

co-located with ICSE 2024

Filter

Role Type

Contributing to

Number of Roles

Country

Results (16)

A Vikram S. AdveAdve, Vikram S. University of Illinois at Urbana-Champaign, USA An Empirical Comparison of Code Generation Approaches for Ansible
Merve AstekinAstekin, Merve Simula Research LaboratoryNorway An Exploratory Study on How Non-Determinism in Large Language Models Affects Log Parsing
C Swarat ChaudhuriChaudhuri, Swarat University of Texas at AustinUnited States Tutorial on Neuro-symbolic Programming
Hetarth ChopraChopra, Hetarth University of Illinois at Urbana-ChampaignUnited States An Empirical Comparison of Code Generation Approaches for Ansible
Aaron CouncilmanCouncilman, Aaron Univ of Illinois Urbana-Champaign An Empirical Comparison of Code Generation Approaches for Ansible
D Benjamin DarnellDarnell, Benjamin University of California, Santa BarbaraUnited States An Empirical Comparison of Code Generation Approaches for Ansible
G David GroveGrove, David IBM ResearchUnited States An Empirical Comparison of Code Generation Approaches for Ansible
H Mark HarmanHarman, Mark Meta Platforms, Inc. and UCLUnited Kingdom Assured LLM-Based Software Engineering
Max HortHort, Max Simula Research LaboratoryNorway An Exploratory Study on How Non-Determinism in Large Language Models Affects Log Parsing
J Reyhaneh JabbarvandJabbarvand, Reyhaneh University of Illinois at Urbana-ChampaignUnited States Session Chair of Late Afternoon Session (part of InteNSE) Session Chair of Afternoon Session (part of InteNSE) Session Chair of Early Morning Session (part of InteNSE) Session Chair of Late Morning Session (part of InteNSE) InteNSE 2024 Closing Remarks
M Leon MoonenMoonen, Leon Simula Research Laboratory and BI Norwegian Business SchoolNorway An Exploratory Study on How Non-Determinism in Large Language Models Affects Log Parsing
P Denys PoshyvanykPoshyvanyk, Denys William & MaryUnited States Towards an Interpretable Science of Deep Learning for Software Engineering: A Causal Inference View
S Atharva SehgalSehgal, Atharva University of Texas, AustinUnited States Tutorial on Neuro-symbolic Programming
T Saeid Tizpaz-NiariTizpaz-Niari, Saeid University of Texas at El PasoUnited States Session Chair of Late Afternoon Session (part of InteNSE) Session Chair of Afternoon Session (part of InteNSE) Session Chair of Early Morning Session (part of InteNSE) Session Chair of Late Morning Session (part of InteNSE)
Y John YangYang, John Princeton SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
Z Shizhuo ZhangZhang, Shizhuo University of Illinois Urbana-Champaign Transformer-Based Models Are Not Yet Perfect At Learning to Emulate Structural Recursion