LLM-based merge conflict resolution performs well on imbalanced conflicts but struggles with large or non-English inputs, while search-based methods show better generalization and strength on balanced conflicts.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.SE 3years
2026 3verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
A systematic mapping study of 248 papers introduces a taxonomy of synergistic effects, inter-analysis workflows, and mapping functions to catalog patterns in combined program analysis techniques.
Frontier LLMs like GPT-5.2 show large accuracy drops on perturbed program-output prediction tasks while open-source reasoning models remain more stable, exposing limits in code semantics understanding.
citing papers explorer
-
LLM-based vs. Search-based Merge Conflict Resolution: An Empirical Study of Competing Paradigms
LLM-based merge conflict resolution performs well on imbalanced conflicts but struggles with large or non-English inputs, while search-based methods show better generalization and strength on balanced conflicts.
-
Combined Program Analysis Techniques: A Systematic Mapping Study
A systematic mapping study of 248 papers introduces a taxonomy of synergistic effects, inter-analysis workflows, and mapping functions to catalog patterns in combined program analysis techniques.
-
How Robustly do LLMs Understand Execution Semantics?
Frontier LLMs like GPT-5.2 show large accuracy drops on perturbed program-output prediction tasks while open-source reasoning models remain more stable, exposing limits in code semantics understanding.