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HFEPX Benchmark Hub

SWE-bench In CS.SE Papers

Updated from current HFEPX corpus (Mar 8, 2026). 4 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Mar 8, 2026). 4 papers are grouped in this benchmark page. Common evaluation modes: Automatic Metrics. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequently cited benchmark: SWE-bench. Common metric signal: pass@1. Use this page to compare protocol setup, judge behavior, and labeling design decisions before running new eval experiments. Newest paper in this set is from Feb 25, 2026.

Papers: 4 Last published: Feb 25, 2026 Global RSS

Researcher Quick Triage

Use this page for benchmark-matched method comparisons and eval protocol selection. Quality band: Developing .

High-Signal Coverage

100.0%

4 / 4 sampled papers are not low-signal flagged.

Replication-Ready Set

1

Papers with explicit benchmark + metric + eval mode fields.

Quality Controls

0.0%

0 papers report calibration/adjudication/IAA controls.

  • 2 papers explicitly name benchmark datasets in the sampled set.
  • 1 papers report at least one metric term in metadata extraction.
  • Start with the ranked shortlist below before reading all papers.

Primary action: Use this page to map benchmark mentions first; wait for stronger metric/QC coverage before strict comparisons.

Why This Matters (Expanded)

Why This Matters For Eval Research

  • automatic metrics appears in 50% of papers in this hub.
  • SWE-bench is a recurring benchmark anchor for cross-paper comparisons in this page.
  • long-horizon tasks appears in 50% of papers, indicating agentic evaluation demand.
Protocol Notes (Expanded)

Protocol Takeaways

  • Quality-control reporting is sparse in this slice; prioritize papers with explicit calibration or adjudication steps.
  • Rater context is mostly domain experts, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.
  • Stratify by benchmark (SWE-bench vs SWE-bench Verified) before comparing methods.

Benchmark Interpretation

  • SWE-bench appears in 100% of hub papers (4/4); use this cohort for benchmark-matched comparisons.
  • SWE-bench Verified appears in 50% of hub papers (2/4); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • pass@1 is reported in 50% of hub papers (2/4); compare with a secondary metric before ranking methods.
  • cost is reported in 25% of hub papers (1/4); compare with a secondary metric before ranking methods.

Start Here (Benchmark-Matched First 6)

Ranked by protocol completeness so you can quickly find papers suitable for comparison studies.

Protocol Matrix (Top 10)

Compare protocol ingredients quickly before deep-reading full papers.

Paper Eval Modes Human Feedback Metrics Quality Controls
SWE-Protégé: Learning to Selectively Collaborate With an Expert Unlocks Small Language Models as Software Engineering Agents

Feb 25, 2026

 Automatic Metrics Not reported Pass@1, Latency Not reported
Structurally Aligned Subtask-Level Memory for Software Engineering Agents

Feb 25, 2026

 Automatic Metrics Not reported Not reported Not reported
SWE-CI: Evaluating Agent Capabilities in Maintaining Codebases via Continuous Integration

Mar 4, 2026

 Not reported Not reported Not reported Not reported
SWE-rebench V2: Language-Agnostic SWE Task Collection at Scale

Feb 27, 2026

 Not reported Not reported Not reported Not reported
Researcher Workflow (Detailed)

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (0% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (0% vs 30% target).

  • Strong: Papers naming benchmarks/datasets

    Coverage is strong (100% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (50% vs 35% target).

  • Moderate: Papers with known rater population

    Coverage is usable but incomplete (25% vs 35% target).

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (25% vs 35% target).

Strengths

  • Most papers provide measurable evaluation context (100% benchmarks, 50% metrics).
  • Agentic evaluation appears in 50% of papers.

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.

Suggested Next Analyses

  • Stratify by benchmark (SWE-bench vs SWE-bench Verified) before comparing methods.
  • Track metric sensitivity by reporting both pass@1 and cost.

Recommended Queries

Benchmark Slice: SWE-benchMetric Slice: pass@1Recent High-Signal Papers
Known Limitations
  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
  • Cross-page comparisons should be benchmark- and metric-matched to avoid protocol confounding.
Research Utility Snapshot (Detailed)

Evaluation Modes

  • Automatic Metrics (2)

Human Feedback Mix

Top Benchmarks

  • SWE Bench (4)
  • SWE Bench Verified (2)
  • SWE Rebench (1)

Top Metrics

  • Pass@1 (2)
  • Cost (1)
  • Latency (1)

Top Papers On This Benchmark

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