Abstract is missing.
- A systematic evaluation of large language models of codeFrank F. Xu, Uri Alon 0002, Graham Neubig, Vincent Josua Hellendoorn. 1-10 [doi]
- A graph neural network-based performance model for deep learning applicationsShikhar Singh, James Hegarty, Hugh Leather, Benoit Steiner. 11-20 [doi]
- Productivity assessment of neural code completionAlbert Ziegler 0001, Eirini Kalliamvakou, X. Alice Li, Andrew Rice, Devon Rifkin, Shawn Simister, Ganesh Sittampalam, Edward Aftandilian. 21-29 [doi]
- From perception to programs: regularize, overparameterize, and amortizeHao Tang, Kevin Ellis. 30-39 [doi]
- Predictive synthesis of API-centric codeDaye Nam, Baishakhi Ray, Seohyun Kim 0001, Xianshan Qu, Satish Chandra 0001. 40-49 [doi]
- ExeBench: an ML-scale dataset of executable C functionsJordi Armengol-Estapé, Jackson Woodruff, Alexander Brauckmann, José Wesley de Souza Magalhães, Michael F. P. O'Boyle. 50-59 [doi]
- Automatically debugging AutoML pipelines using maro: ML automated remediation oracleJulian Dolby, Jason Tsay, Martin Hirzel. 60-69 [doi]
- Syntax-guided program reduction for understanding neural code intelligence modelsMd. Rafiqul Islam Rabin, Aftab Hussain 0001, Mohammad Amin Alipour. 70-79 [doi]