Paper Citation Record · LEDGER

ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2303.07839.

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

pith.paper-citation-record.v1
2303.07839 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:26:32.041733Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T01:49:21.465998Z

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 3912cf41-b336-49eb-8bb4-d812bbc2fff1 · inbound

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation cites this paper.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-24T02:23:46.192371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d9fd823e-7574-46fb-b446-6086a1ac45a6 · inbound

Toward Neurosymbolic Program Comprehension cites this paper.

Toward Neurosymbolic Program Comprehension ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-09T14:26:32.041733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ce5a5be-3920-468d-8983-e24e254fc0f0 · inbound

Using ChatGPT to refine draft conceptual schemata in supply-driven design of multidimensional cubes cites this paper.

Using ChatGPT to refine draft conceptual schemata in supply-driven design of multidimensional cubes ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-09T12:53:37.460223Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea4ce8c4-ef9a-4972-b61d-cf7ce44a1912 · inbound

SnipGen: A Mining Repository Framework for Evaluating LLMs for Code cites this paper.

SnipGen: A Mining Repository Framework for Evaluating LLMs for Code ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-08T14:01:12.387530Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f3301bb-8e52-4a5a-aea1-4dd2c47e008b · inbound

Knowledge-Enhanced Program Repair for Data Science Code cites this paper.

Knowledge-Enhanced Program Repair for Data Science Code ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T20:37:30.834993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36c74ef9-6621-4bd4-9f76-d7ac9c4eb1e8 · inbound

Supporting architecture evaluation for ATAM scenarios with LLMs cites this paper.

Supporting architecture evaluation for ATAM scenarios with LLMs ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T12:13:38.509929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71630dbf-848f-47f6-8200-3717dc8a93e7 · inbound

Development of Automated Software Design Document Review Methods Using Large Language Models cites this paper.

Development of Automated Software Design Document Review Methods Using Large Language Models ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T18:26:17.796397Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a85d9664-245c-42ee-8c87-980833c3b912 · inbound

QUARE: Quality-Aware Requirements Analysis through Multi-Agent Dialectical Negotiation cites this paper.

QUARE: Quality-Aware Requirements Analysis through Multi-Agent Dialectical Negotiation ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-14T22:34:30.254729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a0d414e-1f1a-466a-aa2c-b7d386651037 · inbound

Reliability of Large Language Models for Design Synthesis: An Empirical Study of Variance, Prompt Sensitivity, and Method Scaffolding cites this paper.

Reliability of Large Language Models for Design Synthesis: An Empirical Study of Variance, Prompt Sensitivity, and Method Scaffolding ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-13T22:28:21.169054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f7579230-2d7c-47c1-bd22-066af864014a · inbound

ArgRE: Formal Argumentation for Conflict Resolution in Multi-Agent Requirements Negotiation cites this paper.

ArgRE: Formal Argumentation for Conflict Resolution in Multi-Agent Requirements Negotiation ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:46:10.163999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 38d1bc5a-2b71-4886-841f-f996029bd0ad · inbound

Recommendations for Efficient and Responsible LLM Adoption within Industrial Software Development cites this paper.

Recommendations for Efficient and Responsible LLM Adoption within Industrial Software Development ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:11:27.475913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 19f2cfbb-8d67-4bf9-b577-9f3fe647bcfd · inbound

Using LLMs in Software Design: An Empirical Study of GitHub and A Practitioner Survey cites this paper.

Using LLMs in Software Design: An Empirical Study of GitHub and A Practitioner Survey ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:51:09.001507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation dc8a41d8-72d1-4035-894a-6d68c887ee52 · inbound

Foundation Models as Oracles for Refactoring Correctness Detection cites this paper.

Foundation Models as Oracles for Refactoring Correctness Detection ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:00:36.057199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 97bd8d0e-80db-4932-a4d2-3acc8e9a1af3 · inbound

Foundation Models as Oracles for Refactoring Correctness Detection cites this paper.

Foundation Models as Oracles for Refactoring Correctness Detection ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-07-04T01:49:21.467373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.