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New benchmark evaluates cost-effective AI for code repository exploration

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new benchmark, IssueLoc-Bench, to evaluate the cost-effectiveness of different AI models for repository exploration in coding agent pipelines. The study found that while higher-quality explorers perform best, significantly cheaper models can retain a substantial portion of localization quality while drastically reducing agent time and token usage. The choice of explorer model should be guided by how the localization information will be used downstream, with metrics like ranking and coverage suitable for candidate handoffs, and F1 and exact match for restrictive file gates. AI

IMPACT This research could lead to more efficient and cost-effective AI agents for software development by optimizing the repository exploration phase.

RANK_REASON Research paper introducing a new benchmark and evaluation methodology for AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark evaluates cost-effective AI for code repository exploration

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Research paper introducing a new benchmark and evaluation methodology for AI models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Mohammad Nour Al Awad, Sergey Ivanov ·

    Cost-Effective Repository Exploration for Agentic Issue Localization

    arXiv:2608.29675v1 Announce Type: cross Abstract: Repository exploration is a distinct and costly stage of coding-agent pipelines: before generating a patch, an agent must identify which repository files are likely to matter. We study whether this stage can be delegated to lower-…

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