---
title: "DARE: Difficulty Analysis for Repair Enhancement"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.05797v1"
permalink: "https://feed.astralor.com/items/itm_05346bcf230c77c74be236f0"
published: "2026-11-04T01:10:04.000Z"
score: 60
channel: "tech"
tags: ["LLM","AI Agent","DARE","Difficulty Analysis","Repair Enhancement LLM-based","However","Determining","LLM-based"]
---

# DARE: Difficulty Analysis for Repair Enhancement

## 速览

LLM-based coding agents are primarily evaluated by their solve rates. However, this metric does not reveal how difficult the solved tasks are or account for differences intask difficulty across benchmarks. Determining t…

## 关键点

- LLM-based coding agents are primarily evaluated by their solve rates.
- However, this metric does not reveal how difficult the solved tasks are or account for differences intask difficulty across benchmarks.
- Determining the difficulty of individual tasks by repeatedly running multiple agents is costly, while a predicted difficulty score alone provides limited guidance on what support an agent needs.

## 全文

LLM-based coding agents are primarily evaluated by their solve rates. However, this metric does not reveal how difficult the solved tasks are or account for differences intask difficulty across benchmarks. Determining the difficulty of individual tasks by repeatedly running multiple agents is costly, while a predicted difficulty score alone provides limited guidance on what support an agent needs. To address these challenges, we propose DARE (Difficulty Analysis for Repair Enhancement), which integrates repair-task difficulty measurement, prediction, feature-group contribution analysis, and task-specific repair support. We define continuous task difficulty as the observed failure rate across multiple coding agents. The DARE difficulty model predicts this difficulty using 151 static features extracted from benchmark artifacts. The framework also analyzes the contributions of seven Repair-Task Feature Groups to identify the characteristics most salient to each task and uses this information to select task-specific agent support. We evaluate DARE on four benchmark partitions comprising 2,594 tasks. The difficulty model's aligned predictions achieve Spearman's $ρ$ = .476-.621 on held-out partitions, and the most salient Feature Groups vary across tasks and benchmarks. Furthermore, coding-agent experiments show improved repair success when support is matched to each task's salient Feature Group. These findings demonstrate that difficulty prediction and interpretation can lead to practical improvements in automated program repair (APR), while also providing a basis for difficulty-stratified agent evaluation, cross-benchmark comparisons, and identifying areas of repair capability that warrant further improvement.

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