30 citations · 78 across the 23 of their papers we have counts for
26 papers
A Systematic Study of Time Limit Exceeded Errors in Online Programming Assignments
Jialu Zhang, Jialiang Gu, Wangmeiyu Zhang +5
Online programming platforms such as Codeforces and LeetCode attract millions of users seeking to learn to program or refine their skills for industry interviews. A major challenge…
Abstain and Validate: A Dual-LLM Policy for Reducing Noise in Agentic Program Repair
José Cambronero, Michele Tufano, Sherry Shi +7
Agentic Automated Program Repair (APR) is increasingly tackling complex, repository-level bugs in industry, but ultimately these patches still need to be reviewed by a human before…
Towards Verified Code Reasoning by LLMs
Meghana Sistla, Gogul Balakrishnan, Pat Rondon +3
While LLM-based agents are able to tackle a wide variety of code reasoning questions, the answers are not always correct. This prevents the agent from being useful in situations wh…
Tabularis Formatus: Predictive Formatting for Tables
Mukul Singh, José Cambronero, Sumit Gulwani +2
Spreadsheet manipulation software are widely used for data management and analysis of tabular data, yet the creation of conditional formatting (CF) rules remains a complex task req…
Agentic Bug Reproduction for Effective Automated Program Repair at Google
Runxiang Cheng, Michele Tufano, Jürgen Cito +5
Bug reports often lack sufficient detail for developers to reproduce and fix the underlying defects. Bug Reproduction Tests (BRTs), tests that fail when the bug is present and pass…
Evaluating Agent-based Program Repair at Google
Pat Rondon, Renyao Wei, José Cambronero +5
Agent-based program repair offers to automatically resolve complex bugs end-to-end by combining the planning, tool use, and code generation abilities of modern LLMs. Recent work ha…
An Empirical Study of Validating Synthetic Data for Formula Generation
Usneek Singh, José Cambronero, Sumit Gulwani +5
Large language models (LLMs) can be leveraged to help with writing formulas in spreadsheets, but resources on these formulas are scarce, impacting both the base performance of pre-…
Solving Data-centric Tasks using Large Language Models
Shraddha Barke, Christian Poelitz, Carina Suzana Negreanu +10
Large language models (LLMs) are rapidly replacing help forums like StackOverflow, and are especially helpful for non-professional programmers and end users. These users are often…
Assessing GPT4-V on Structured Reasoning Tasks
Mukul Singh, José Cambronero, Sumit Gulwani +2
Multi-modality promises to unlock further uses for large language models. Recently, the state-of-the-art language model GPT-4 was enhanced with vision capabilities. We carry out a…
CodeFusion: A Pre-trained Diffusion Model for Code Generation
Mukul Singh, José Cambronero, Sumit Gulwani +3
Imagine a developer who can only change their last line of code, how often would they have to start writing a function from scratch before it is correct? Auto-regressive models for…
FormaT5: Abstention and Examples for Conditional Table Formatting with Natural Language
Mukul Singh, José Cambronero, Sumit Gulwani +5
Formatting is an important property in tables for visualization, presentation, and analysis. Spreadsheet software allows users to automatically format their tables by writing data-…
Tabular Representation, Noisy Operators, and Impacts on Table Structure Understanding Tasks in LLMs
Ananya Singha, José Cambronero, Sumit Gulwani +2
Large language models (LLMs) are increasingly applied for tabular tasks using in-context learning. The prompt representation for a table may play a role in the LLMs ability to proc…
Co-audit: tools to help humans double-check AI-generated content
Andrew D. Gordon, Carina Negreanu, José Cambronero +9
Users are increasingly being warned to check AI-generated content for correctness. Still, as LLMs (and other generative models) generate more complex output, such as summaries, tab…
Automating Human Tutor-Style Programming Feedback: Leveraging GPT-4 Tutor Model for Hint Generation and GPT-3.5 Student Model for Hint Validation
Tung Phung, Victor-Alexandru Pădurean, Anjali Singh +5
Generative AI and large language models hold great promise in enhancing programming education by automatically generating individualized feedback for students. We investigate the r…
DataVinci: Learning Syntactic and Semantic String Repairs
Mukul Singh, José Cambronero, Sumit Gulwani +3
String data is common in real-world datasets: 67.6% of values in a sample of 1.8 million real Excel spreadsheets from the web were represented as text. Systems that successfully cl…
Demonstration of CORNET: A System For Learning Spreadsheet Formatting Rules By Example
Mukul Singh, Jose Cambronero, Sumit Gulwani +3
Data management and analysis tasks are often carried out using spreadsheet software. A popular feature in most spreadsheet platforms is the ability to define data-dependent formatt…
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…
FLAME: A small language model for spreadsheet formulas
Harshit Joshi, Abishai Ebenezer, José Cambronero +5
Spreadsheets are a vital tool for end-user data management. Using large language models for formula authoring assistance in these environments can be difficult, as these models are…
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…
LoopStack: a Lightweight Tensor Algebra Compiler Stack
Bram Wasti, José Pablo Cambronero, Benoit Steiner +2
We present LoopStack, a domain specific compiler stack for tensor operations, composed of a frontend, LoopTool, and an efficient optimizing code generator, LoopNest. This stack ena…
Searching for Replacement Classes
Malavika Samak, Jose Pablo Cambronero, Martin C. Rinard
Software developers must often replace existing components in their systems to adapt to evolving environments or tooling. While traditional code search systems are effective at ret…
Inferring Drop-in Binary Parsers from Program Executions
Thurston H. Y. Dang, Jose P. Cambronero, Martin C. Rinard
We present BIEBER (Byte-IdEntical Binary parsER), the first system to model and regenerate a full working parser from instrumented program executions. To achieve this, BIEBER explo…
Characterizing Developer Use of Automatically Generated Patches
José Pablo Cambronero, Jiasi Shen, Jürgen Cito +2
We present a study that characterizes the way developers use automatically generated patches when fixing software defects. Our study tasked two groups of developers with repairing…