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Paper Citation Record · LEDGER

Automated Test Case Repair Using Language Models

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2401.06765.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2401.06765 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T19:07:12.629017Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-11T10:27:18.310351Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation c8be32e1-2a6d-41ae-baab-cd95d229ced8 · inbound

REACCEPT: Automated Co-evolution of Production and Test Code Based on Dynamic Validation and Large Language Models cites this paper.

REACCEPT: Automated Co-evolution of Production and Test Code Based on Dynamic Validation and Large Language Models Automated Test Case Repair Using Language Models

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-12T19:07:12.629017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T19:07:12.629017Z digest=sha256:b41e0d5104b7582341ccb65f8e80dbeaa92f94fdb9cb87db9e9046d4cc85c729

Observation a4a5747a-dd55-4b34-8266-899e61260db8 · inbound

A Large-scale Empirical Study on Fine-tuning Large Language Models for Unit Testing cites this paper.

A Large-scale Empirical Study on Fine-tuning Large Language Models for Unit Testing Automated Test Case Repair Using Language Models

Reference 53

Resolution
verified exact
local_arxiv, observed 2026-08-11T10:27:18.317341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T10:27:18.057826Z digest=sha256:03816951e3607f3b478d0c1e49afca5a03bf76a383b9af6679643901c0d39ab7