Pith. sign in

Paper Citation Record · LEDGER

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

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 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 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:00:46.169447Z

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-12T05:12:22.013365Z digest=sha256:621f5c9165234325704eb553cf0c9cc905cb23148af1b5d8cf217d86d6246b71

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-23T02:42:59.190220Z digest=sha256:6c3461e3e652840df9bd436fe8f06be87171df87c9ec37d382c69dda5674bb9c

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-14T22:52:58.524934Z digest=sha256:1543a252f93056fc69d73d9fc5420f26896289b01f16a58d8c41c19715b8eec7

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-10T04:21:39.637962Z digest=sha256:cb422c589e0a084d8e6b379030de2751244ddd41c61486f676f52404f8793742

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-08T03:18:15.635912Z digest=sha256:a59e6aef33139e316938096d89804d3aa54cfd99507216f24e41efbf4e05e014

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-07T16:04:48.394294Z digest=sha256:b6eae5d4917453ad7c9c3a82838f2cb5175b346ddf8ed133339e8cb3081a8320

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-26T09:47:51.579030Z digest=sha256:c5015af42a5b3c81b41b583270ce18708831bb9048bf7027c57b55014574a90a

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