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.
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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.
Secondary analysis of 30 Saudi Google users' interviews identifies balanced perceptions of activity logs spanning benefits, risks, misconceptions, and severe negative outcomes.
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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.
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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.
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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.
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Users' Activity Logs: the Good, the Bad, the Misconception, and the Disastrous
Secondary analysis of 30 Saudi Google users' interviews identifies balanced perceptions of activity logs spanning benefits, risks, misconceptions, and severe negative outcomes.