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Paper Citation Record · LEDGER

An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2302.06527.

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

pith.paper-citation-record.v1
2302.06527 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T14:53:28.940188Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T09:39:46.017724Z

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 1712bd60-e7f5-4c76-98f3-c4bc8c94dd7a · inbound

Agentless: Demystifying LLM-based Software Engineering Agents cites this paper.

Agentless: Demystifying LLM-based Software Engineering Agents An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation

Reference 87

Resolution
verified exact
arxiv_id, observed 2026-05-12T05:12:22.208403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-12T05:12:22.013365Z digest=sha256:5006242ee9d5892d9bd33148c9583b5fc385af8cd7af22690497a34ada304be9

Observation d4179e8f-18d2-47f5-be85-e0fc286e9fc8 · inbound

Combining Large Language Models with Static Analyzers for Code Review Generation cites this paper.

Combining Large Language Models with Static Analyzers for Code Review Generation An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-08T14:53:28.940188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T14:53:28.940188Z digest=sha256:5973c36ba76a6cac437fc72081f69069f95a65dc8c430bb44c2694e7e60599d4

Observation 2b851e5b-7267-48e4-b9cb-623ac4ee0f19 · inbound

ClassInvGen: Class Invariant Synthesis using Large Language Models cites this paper.

ClassInvGen: Class Invariant Synthesis using Large Language Models An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation

Reference 40

Resolution
verified exact
arxiv_id, observed 2026-05-23T02:45:19.500051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-23T02:42:59.190220Z digest=sha256:c2114cc2a9c0a7a0a176e7e7ad5a9027fed6b4680692d0e39463ca7f3e82172b

Observation 48c9b081-415b-456b-b727-1b4e39ad7f61 · inbound

SAGE:Specification-Aware Grammar Extraction for Automated Test Case Generation with LLMs cites this paper.

SAGE:Specification-Aware Grammar Extraction for Automated Test Case Generation with LLMs An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-07T11:00:46.169447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:00:46.169447Z digest=sha256:d7f1b7414c7b383bace50db06d652a721628f3201a10fefb9bc4617b1844da88

Observation 8a28d386-ebd3-4d15-a633-93225a0871ff · inbound

In-Context Learning as an Effective Estimator of Functional Correctness of LLM-Generated Code cites this paper.

In-Context Learning as an Effective Estimator of Functional Correctness of LLM-Generated Code An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-06T19:34:36.206618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:34:36.206618Z digest=sha256:803b46d5a8477ea7441d0afb8be1684ba676e6d64f3a00f9580602e85fda220c

Observation c91c5c03-7346-42f6-a3b8-54024eb54703 · inbound

Ensemble-Based Uncertainty Estimation for Code Correctness Estimation cites this paper.

Ensemble-Based Uncertainty Estimation for Code Correctness Estimation An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation

Reference 38

Resolution
verified exact
arxiv_id, observed 2026-05-14T22:53:14.206666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-14T22:52:58.524934Z digest=sha256:f5f046283bc9fd4e316f045829fb588ddbd1b2762b8d70158bdea515447c4172

Observation b9649ee5-71c1-4e4a-a400-7d2ad476d253 · inbound

Co-Located Tests, Better AI Code: How Test Syntax Structure Affects Foundation Model Code Generation cites this paper.

Co-Located Tests, Better AI Code: How Test Syntax Structure Affects Foundation Model Code Generation An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-05-11T12:01:05.166984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T04:21:39.637962Z digest=sha256:a02d1f4ea00c6a0d3ba4b49752fd2ae2b2b0d6c129f7a0e2f40e5d06468fadd9

Observation e26f4893-de0b-4bfe-bf50-c48c80eef25e · inbound

On the Footprints of Reviewer Bots Feedback on Agentic Pull Requests in OSS GitHub Repositories cites this paper.

On the Footprints of Reviewer Bots Feedback on Agentic Pull Requests in OSS GitHub Repositories An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-11T22:11:13.536297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-08T03:18:15.635912Z digest=sha256:4c4433f7a2220fc88bd2c6c793a5e61cd2723cbeab2fc57f57bd8271b9dd2971

Observation 552330ad-5cb7-4f50-85b6-6518eca05289 · inbound

POSTCONDBENCH: Benchmarking Correctness and Completeness in Formal Postcondition Inference cites this paper.

POSTCONDBENCH: Benchmarking Correctness and Completeness in Formal Postcondition Inference An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation

Reference 112

Resolution
verified exact
arxiv_id, observed 2026-05-11T23:56:12.277153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-07T16:04:48.394294Z digest=sha256:508db9f97deecb2572df57eac3fc5d110f15b15895896d3f11b42a1996ee7222

Observation 9aaecfb4-8b5c-412b-b79b-ed880090ad83 · inbound

VeriPort: Automated and Verified Patch Backporting at Scale cites this paper.

VeriPort: Automated and Verified Patch Backporting at Scale An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation

Reference 39

Resolution
verified exact
arxiv_id, observed 2026-07-04T09:39:46.019260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-26T09:47:51.579030Z digest=sha256:e41d34b4a1a5df2bd9b52cec8b51f298b6afa0568f2e1522eebbfa38e7000eec

Observation 693eb81b-ba9b-48e2-aa95-db5bfc04361a · inbound

Adversarial Test-Hardening for AI-Written Code: An Instrument Autopsy and a Pre-Registered Causal Estimate of the Critic Loop cites this paper.

Adversarial Test-Hardening for AI-Written Code: An Instrument Autopsy and a Pre-Registered Causal Estimate of the Critic Loop An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-01T03:57:40.753104Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T03:57:40.753104Z digest=sha256:9e290b163c70226b4305021a6352c2fd733e750218332a9ed51340bd299fb4c9