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20172025
most citedFairness-guided SMT-based Rectification of Decision Trees and Random Forests

1 citations · 2 across the 4 of their papers we have counts for

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5 papers · 1 filter

cs.SE20251 cited

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…

cs.SE2025

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…

cs.SE2024

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 (…

cs.SE2023

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,…

cs.SE2017

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…