Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design
As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2303.07839.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-03T06:30:56.289259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-14T22:34:30.254729Z
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 2026-08-03T06:30:56.289259+00:00.
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 2026-08-03T06:30:56.289259+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 2026-08-03T06:30:56.289259+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 2026-08-03T06:30:56.289259+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 2026-08-03T06:30:56.289259+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 2026-08-03T06:30:56.289259+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 2026-08-03T06:30:56.289259+00:00.