66 citations · 98 across the 4 of their papers we have counts for
5 papers
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…
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…
Multi-modal Program Inference: a Marriage of Pre-trainedLanguage Models and Component-based Synthesis
Kia Rahmani, Mohammad Raza, Sumit Gulwani +5
Multi-modal program synthesis refers to the task of synthesizing programs (code) from their specification given in different forms, such as a combination of natural language and ex…
Learning Quick Fixes from Code Repositories
Reudismam Rolim, Gustavo Soares, Rohit Gheyi +2
Code analyzers such as Error Prone and FindBugs detect code patterns symptomatic of bugs, performance issues, or bad style. These tools express patterns as quick fixes that detect…
TraceDiff: Debugging Unexpected Code Behavior Using Trace Divergences
Ryo Suzuki, Gustavo Soares, Andrew Head +5
Recent advances in program synthesis offer means to automatically debug student submissions and generate personalized feedback in massive programming classrooms. When automatically…