Optimizing Alloy for Multi-objective Software Product Line Configuration. - Ed Zulkoski; Chris Kleynhans; Ming-Ho Yee; Derek Rayside; Krzysztof Czarnecki 001 (ABZ, 2014)

From Datalog to flix: a declarative language for fixed points on lattices. - Magnus Madsen; Ming-Ho Yee; Ondrej Lhoták (PLDI, 2016)

Optimizing Contractor Selection for Construction Packages in Capital Projects. - Mahdi Safa; Ming-Ho Yee; Derek Rayside; Carl T. Haas (J. Comput. Civ. Eng., 2016)

Correctness of speculative optimizations with dynamic deoptimization. - Olivier Flückiger; Gabriel Scherer; Ming-Ho Yee; Aviral Goel; Amal Ahmed 001; Jan Vitek (Proc. ACM Program. Lang., 2018)

R melts brains: an IR for first-class environments and lazy effectful arguments. - Olivier Flückiger; Guido Chari; Jan Jecmen; Ming-Ho Yee; Jakob Hain; Jan Vitek (DLS, 2019)

Precise Dataflow Analysis of Event-Driven Applications. - Ming-Ho Yee; Ayaz Badouraly; Ondrej Lhoták; Frank Tip; Jan Vitek (CoRR, 2019)

Contextual dispatch for function specialization. - Olivier Flückiger; Guido Chari; Ming-Ho Yee; Jan Jecmen; Jakob Hain; Jan Vitek (Proc. ACM Program. Lang., 2020)

A Scalable and Extensible Approach to Benchmarking NL2Code for 18 Programming Languages. - Federico Cassano; John Gouwar; Daniel Nguyen; Sydney Nguyen; Luna Phipps-Costin; Donald Pinckney; Ming-Ho Yee; Yangtian Zi; Carolyn Jane Anderson; Molly Q. Feldman; Arjun Guha; Michael Greenberg 002; et al. (CoRR, 2022)

Do Machine Learning Models Produce TypeScript Types That Type Check? - Ming-Ho Yee; Arjun Guha (ECOOP, 2023)

Type Prediction With Program Decomposition and Fill-in-the-Type Training. - Federico Cassano; Ming-Ho Yee; Noah Shinn; Arjun Guha; Steven Holtzen (CoRR, 2023)

Do Machine Learning Models Produce TypeScript Types That Type Check? (Artifact). - Ming-Ho Yee; Arjun Guha (Dagstuhl Artifacts Ser., 2023)

StarCoder: may the source be with you! - Raymond Li; Loubna Ben Allal; Yangtian Zi; Niklas Muennighoff; Denis Kocetkov; Chenghao Mou; Marc Marone; Christopher Akiki; Jia Li 015; Jenny Chim; Qian Liu 012; Evgenii Zheltonozhskii; et al. (Trans. Mach. Learn. Res., 2023)

MultiPL-E: A Scalable and Polyglot Approach to Benchmarking Neural Code Generation. - Federico Cassano; John Gouwar; Daniel Nguyen; Sydney Nguyen; Luna Phipps-Costin; Donald Pinckney; Ming-Ho Yee; Yangtian Zi; Carolyn Jane Anderson; Molly Q. Feldman; Arjun Guha; Michael Greenberg 002; et al. (IEEE Trans. Software Eng., 2023)