66 citations · 128 across the 12 of their papers we have counts for
5 papers · 1 filter
Repairing Bugs in Python Assignments Using Large Language Models
Jialu Zhang, José Cambronero, Sumit Gulwani +4
Students often make mistakes on their introductory programming assignments as part of their learning process. Unfortunately, providing custom repairs for these mistakes can require…
Repair Is Nearly Generation: Multilingual Program Repair with LLMs
Harshit Joshi, José Cambronero, Sumit Gulwani +3
Most programmers make mistakes when writing code. Some of these mistakes are small and require few edits to the original program -- a class of errors recently termed last mile mist…
CORNET: Learning Table Formatting Rules By Example
Mukul Singh, José Cambronero, Sumit Gulwani +4
Spreadsheets are widely used for table manipulation and presentation. Stylistic formatting of these tables is an important property for both presentation and analysis. As a result,…
Neurosymbolic Repair for Low-Code Formula Languages
Rohan Bavishi, Harshit Joshi, José Pablo Cambronero Sánchez +5
Most users of low-code platforms, such as Excel and PowerApps, write programs in domain-specific formula languages to carry out nontrivial tasks. Often users can write most of the…
Synchromesh: Reliable code generation from pre-trained language models
Gabriel Poesia, Oleksandr Polozov, Vu Le +4
Large pre-trained language models have been used to generate code,providing a flexible interface for synthesizing programs from natural language specifications. However, they often…