66 citations · 147 across the 8 of their papers we have counts for
9 papers
Generative AI for Programming Education: Benchmarking ChatGPT, GPT-4, and Human Tutors
Tung Phung, Victor-Alexandru Pădurean, José Cambronero +5
Generative AI and large language models hold great promise in enhancing computing education by powering next-generation educational technologies for introductory programming. Recen…
Generating High-Precision Feedback for Programming Syntax Errors using Large Language Models
Tung Phung, José Cambronero, Sumit Gulwani +4
Large language models (LLMs), such as Codex, hold great promise in enhancing programming education by automatically generating feedback for students. We investigate using LLMs to g…
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…
Overwatch: Learning Patterns in Code Edit Sequences
Yuhao Zhang, Yasharth Bajpai, Priyanshu Gupta +8
Integrated Development Environments (IDEs) provide tool support to automate many source code editing tasks. Traditionally, IDEs use only the spatial context, i.e., the location whe…
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…
Learning Syntactic Program Transformations from Examples
Reudismam Rolim, Gustavo Soares, Loris D'Antoni +5
IDEs, such as Visual Studio, automate common transformations, such as Rename and Extract Method refactorings. However, extending these catalogs of transformations is complex and ti…