4 citations · 6 across the 5 of their papers we have counts for
6 papers
Automated Repair of Ambiguous Problem Descriptions for LLM-Based Code Generation
Haoxiang Jia, Robbie Morris, He Ye +2
The growing use of large language models (LLMs) has increased the importance of natural language (NL) in software engineering. However, ambiguity of NL can harm software quality, a…
HoarePrompt: Structural Reasoning About Program Correctness in Natural Language
Dimitrios Stamatios Bouras, Yihan Dai, Tairan Wang +2
While software requirements are often expressed in natural language, verifying the correctness of a program against such requirements is a hard and underexplored problem. Large lan…
The Fact Selection Problem in LLM-Based Program Repair
Nikhil Parasaram, Huijie Yan, Boyu Yang +5
Recent research has shown that incorporating bug-related facts, such as stack traces and GitHub issues, into prompts enhances the bug-fixing capabilities of large language models (…
User-Centric Deployment of Automated Program Repair at Bloomberg
David Williams, James Callan, Serkan Kirbas +4
Automated program repair (APR) tools have unlocked the potential for the rapid rectification of codebase issues. However, to encourage wider adoption of program repair in practice,…
Fairness-guided SMT-based Rectification of Decision Trees and Random Forests
Jiang Zhang, Ivan Beschastnikh, Sergey Mechtaev +1
Data-driven decision making is gaining prominence with the popularity of various machine learning models. Unfortunately, real-life data used in machine learning training may captur…
Partitioning Patches into Test-equivalence Classes for Scaling Program Repair
Sergey Mechtaev, Xiang Gao, Shin Hwei Tan +1
Automated program repair is a problem of finding a transformation (called a patch) of a given incorrect program that eliminates the observable failures. It has important applicatio…