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Paper Citation Record · LEDGER

Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 32 inbound Pith citation observations for arXiv:2304.10778.

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

pith.paper-citation-record.v1
2304.10778 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 32 of 32 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 32 of 32 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:01:10.524442Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

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

External citation measurements

68
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation a3f13fc8-8b45-49ed-bace-4b3494f1e763 · 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 Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 29

Resolution
verified exact
arxiv_id, observed 2026-05-24T02:23:46.135108Z

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=2026-05-24T02:19:23.135463Z digest=sha256:06a8a8a4296a121663d99b97648974e9b57a4b0efd784e1f9a41049b01327e69

Observation 782a917c-c3f4-4d76-9ec9-3bc0aad8c8bc · inbound

Large Language Model-Based Agents for Software Engineering: A Survey cites this paper.

Large Language Model-Based Agents for Software Engineering: A Survey Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-17T12:35:48.586291Z

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=2026-05-17T12:35:48.170947Z digest=sha256:ea0f88780d40d8e4059f5d0004e82f018b3cb0504a8a15ab446716eced0cd12a

Observation 2370909f-31e6-49eb-bdfa-c3d92dcf1b73 · inbound

Eradicating the Unseen: Detecting, Exploiting, and Remediating a Path Traversal Vulnerability across GitHub cites this paper.

Eradicating the Unseen: Detecting, Exploiting, and Remediating a Path Traversal Vulnerability across GitHub Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 125

Resolution
unresolved
no resolver link, observed 2026-08-07T14:01:10.524442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:01:10.524442Z digest=sha256:524ebc6529d6cc0d4c3cd265456072b7241ef78665c7deb42d88bfaa289682e6

Observation ea630df8-ea98-478a-a14c-c569e096c2dd · inbound

Do Generative AI Tools Ensure Green Code? An Investigative Study cites this paper.

Do Generative AI Tools Ensure Green Code? An Investigative Study Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-07T05:04:38.131274Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:04:38.131274Z digest=sha256:71420144344d06b3f5b93e5658cd403e2885075d5c07b907668715b6a8669587

Observation 9da3e49d-a12e-44d9-b41c-d7f49378169f · inbound

Quality Assessment of Python Tests Generated by Large Language Models cites this paper.

Quality Assessment of Python Tests Generated by Large Language Models Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-07T00:22:57.143131Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:22:57.143131Z digest=sha256:04ad71438c8d3c6804653107f401a8a959035db8eff4eb4b3ef532080a9c22b5

Observation 47f5486b-76b8-41db-82a6-80dd5d887dcf · inbound

Evaluating Efficiency and Novelty of LLM-Generated Code for Graph Analysis cites this paper.

Evaluating Efficiency and Novelty of LLM-Generated Code for Graph Analysis Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-06T19:09:34.032611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:09:34.032611Z digest=sha256:b96bff4273da1473a50f617f1d076e7199e80bf1a056a76d3c3751f6cfa53d4e

Observation 0aed5b4b-8f70-4b6c-b77e-b74404cbfc73 · inbound

Is Quantization a Deal-breaker? Empirical Insights from Large Code Models cites this paper.

Is Quantization a Deal-breaker? Empirical Insights from Large Code Models Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-06T17:56:21.332123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:56:21.332123Z digest=sha256:f611cbd7b55b3b3884cfe5ca2082b48a25c95ed662f4a93a0251e41f01ef639c

Observation 72ecfa1c-470b-4af0-b922-86c1d5712d6f · inbound

Human-Written vs. AI-Generated Code: A Large-Scale Study of Defects, Vulnerabilities, and Complexity cites this paper.

Human-Written vs. AI-Generated Code: A Large-Scale Study of Defects, Vulnerabilities, and Complexity Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-05T14:09:55.151998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:09:55.151998Z digest=sha256:a73fe7092c8fae3d7ae8d69ffd14d0cf9fad84782ff96a32602dd348c07b65ee

Observation 91397d05-6cc8-4774-b395-391f63d70582 · inbound

Secure Code Generation at Scale with Reflexion cites this paper.

Secure Code Generation at Scale with Reflexion Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-03T23:50:48.870152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T23:50:48.870152Z digest=sha256:5dbccd0c583c7427375535d905e8878ec30f40c5555cbd39f66d8d8953f3deeb

Observation d95378ad-5796-4592-8880-21dcebda23d0 · inbound

WildCode Revisited: A Comprehensive Empirical Study on the Security of LLM-Generated Code cites this paper.

WildCode Revisited: A Comprehensive Empirical Study on the Security of LLM-Generated Code Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-03T18:41:21.046690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T18:41:21.046690Z digest=sha256:012f9c5fd6e58b1f6494cdb4fb65899253c99a4d620640207c821ee5bc9c646a

Observation 1c457c1b-075d-42b7-b2cf-100f5fae9515 · inbound

Developers' Experience with Generative AI -- First Insights from an Empirical Mixed-Methods Field Study cites this paper.

Developers' Experience with Generative AI -- First Insights from an Empirical Mixed-Methods Field Study Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-03T14:36:05.053801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T14:36:05.053801Z digest=sha256:47d534dbe1d2b5d6d33573c2b7ce9bdcbd7077cb58f4e1f780a3658f0b12f176

Observation 5a65d466-b3a8-46ed-84be-a43604fa4358 · inbound

Beyond Verifiable Rewards: Rubric-Based GRM for Reinforced Fine-Tuning SWE Agents cites this paper.

Beyond Verifiable Rewards: Rubric-Based GRM for Reinforced Fine-Tuning SWE Agents Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 33

Resolution
verified exact
arxiv_id, observed 2026-05-15T12:25:35.816573Z

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=2026-05-15T12:22:13.551709Z digest=sha256:799c30ea9456704deb0833bf0ee23720e0f532bb3d89d886b355d8bbdaa33dfd

Observation abf424bd-55fa-4884-b37c-d915692abc33 · inbound

How Do Developers Interact with AI? An Exploratory Study on Modeling Developer Programming Behavior cites this paper.

How Do Developers Interact with AI? An Exploratory Study on Modeling Developer Programming Behavior Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 71

Resolution
verified exact
arxiv_id, observed 2026-05-14T22:13:03.187010Z

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=2026-05-14T22:12:39.662344Z digest=sha256:5ed3ab6739a27cd68ab1cfef78adbd1836a289e1b157076b3d140e7c4c5d5cc5

Observation 5d8d6985-83c1-4851-a97a-189ba6c69d82 · inbound

A Longitudinal Analysis of Good First Issue Practices and Newcomer Pull Requests in Popular OSS Projects cites this paper.

A Longitudinal Analysis of Good First Issue Practices and Newcomer Pull Requests in Popular OSS Projects Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 22

Resolution
verified exact
arxiv_id, observed 2026-05-12T09:36:26.359139Z

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=2026-05-07T10:22:16.502977Z digest=sha256:8b6188172857e1821dc7caba126f1f335e9a905f8d774dec7a3790de86dc65bf

Observation 42380031-75ce-46a2-9cdd-a3dd7dc4a604 · inbound

Social Bias in LLM-Generated Code: Benchmark and Mitigation cites this paper.

Social Bias in LLM-Generated Code: Benchmark and Mitigation Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 166

Resolution
verified exact
arxiv_id, observed 2026-05-11T15:36:08.664051Z

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=2026-05-09T19:34:51.433422Z digest=sha256:d8920b58b2d0a5de1cb04b88a4039aec05b93dbb04be8afcfca662f45c2f6f0a

Observation ead6af44-bc68-49ed-b57b-2252c5cad799 · inbound

Bridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Code cites this paper.

Bridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Code Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 144

Resolution
verified exact
arxiv_id, observed 2026-05-11T17:26:04.532845Z

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=2026-05-08T17:37:51.790000Z digest=sha256:7f0f695f412fd0eb7a7e93e335a96943ac573caaac776de3baf48c50aeea76f8

Observation a7e81a2c-790a-406d-b2cc-8247207dc878 · inbound

Context-Augmented Code Generation: How Product Context Improves AI Coding Agent Decision Compliance by 49% cites this paper.

Context-Augmented Code Generation: How Product Context Improves AI Coding Agent Decision Compliance by 49% Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-05-12T07:51:48.713785Z

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=2026-05-12T01:33:26.870356Z digest=sha256:2a0d3826b697947dc88cfc3a27af8e60836fbdf8df8f20d0dee1878549fd5f2d

Observation 5b736f39-a10e-49b4-8e67-e53a712126ca · inbound

LLM Translation of Compiler Intermediate Representation cites this paper.

LLM Translation of Compiler Intermediate Representation Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 46

Resolution
verified exact
arxiv_id, observed 2026-05-12T08:36:23.986923Z

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=2026-05-12T00:59:05.006846Z digest=sha256:3a1a21697fd20c4825a85fec22742f4e3326cb55149195919d686b8858a409f2

Observation 7160c5ee-8e62-4740-b672-920004c215c2 · inbound

Revisiting DAgger in the Era of LLM-Agents cites this paper.

Revisiting DAgger in the Era of LLM-Agents Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 44

Resolution
verified exact
arxiv_id, observed 2026-05-14T19:57:53.334730Z

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=2026-05-14T19:56:06.762156Z digest=sha256:603790f5506674ea310eba1ef86d134abe63caefbba71532b4ae94a5f481075a

Observation 02c7bacd-5822-4fd3-90c3-0749139f913a · inbound

Assistance to Autonomy: A Systematic Literature Review of Agentic AI across the Software Development Life Cycle cites this paper.

Assistance to Autonomy: A Systematic Literature Review of Agentic AI across the Software Development Life Cycle Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 33

Resolution
verified exact
arxiv_id, observed 2026-05-19T16:27:39.536761Z

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=2026-05-19T16:27:05.280664Z digest=sha256:273c2064957c4f3fc0dfb050784504294d63b77066bf3bf342105c6a3b7fb7f8

Observation e2bf9ea6-586b-497d-820a-3056a6e9cfc3 · inbound

The Impact of AI Coding Assistants on Software Engineering: A Longitudinal Study cites this paper.

The Impact of AI Coding Assistants on Software Engineering: A Longitudinal Study Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 52

Resolution
verified exact
arxiv_id, observed 2026-05-25T04:10:19.405479Z

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=2026-05-25T04:09:32.859538Z digest=sha256:59a6086ced630528d46cfdfc7c6d177c41cb508d541ac4fc595d14fe05ddb4ec

Observation 0d790177-c688-46f4-a823-bb8c71da0c47 · inbound

An Empirical Evaluation of LLM-Generated Code Security Across Prompting Methods cites this paper.

An Empirical Evaluation of LLM-Generated Code Security Across Prompting Methods Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 45

Resolution
metadata mismatch
arxiv_id, observed 2026-06-30T15:34:48.545994Z

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=2026-06-30T15:17:26.306332Z digest=sha256:87bc97572074de7a2c72a67e2e47103353410c84e48ca0b28606c6ae8d6979b7

Observation b495116f-2720-4b29-96ff-d75ee996fcb3 · inbound

Enhancing Reliability in LLM-Based Secure Code Generation cites this paper.

Enhancing Reliability in LLM-Based Secure Code Generation Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-06-30T15:14:46.819405Z

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=2026-06-30T15:14:02.156588Z digest=sha256:39b4b60d439436085a31730f9394d08bb54c32cdf8c4c9a83ddf44a94a38cf91

Observation 0983d97b-a03b-4077-8ef8-139e3dbfb646 · inbound

From Prompting to Verification: How Experience Shapes Vibe Coding Practices cites this paper.

From Prompting to Verification: How Experience Shapes Vibe Coding Practices Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 79

Resolution
verified exact
arxiv_id, observed 2026-06-30T13:14:40.443345Z

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=2026-06-30T13:13:31.583222Z digest=sha256:1e33b7f5a327c52db781d654d4307e3a549dff9616d17a9a97ce273fd0b27d95

Observation 4f993f05-eb35-474b-bf78-30c27b9efe2d · inbound

Short-Term Gain, Long-Term Fragility: AI Labor Substitution and the Erosion of Sustainable Capability cites this paper.

Short-Term Gain, Long-Term Fragility: AI Labor Substitution and the Erosion of Sustainable Capability Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-07-05T01:40:34.395921Z

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=2026-07-05T01:39:02.068361Z digest=sha256:a562924c1c134f7700f5090389db84a055a6142656e9fc015368c1665b122585

Observation ecdfa141-efee-49c2-badf-100bc1cf27ca · inbound

Vibe Coding Ate My Homework: An evaluation of AI approaches to greenfield software engineering and programming cites this paper.

Vibe Coding Ate My Homework: An evaluation of AI approaches to greenfield software engineering and programming Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 31

Resolution
verified exact
arxiv_id, observed 2026-07-03T18:38:49.553754Z

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=2026-06-27T02:38:26.858639Z digest=sha256:6881d32eecbcb4e1cfb0abb2b2c429ff9ce25d82a1ef511a9f7aa3a1a3c38aff

Observation 10083c92-43cd-4d7a-a759-65aaaf1704b3 · inbound

Vibe Coding Ate My Homework: An evaluation of AI approaches to greenfield software engineering and programming cites this paper.

Vibe Coding Ate My Homework: An evaluation of AI approaches to greenfield software engineering and programming Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 31

Resolution
verified exact
arxiv_id, observed 2026-07-02T22:37:25.387141Z

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=2026-07-02T22:33:44.537174Z digest=sha256:e9713095e1afa6689355cd357790bbf28a8b6b6b3d51402d0a5d3e8a48fb50cc

Observation e2387973-e3f3-4570-a290-88635a207d13 · inbound

Qiskit Code Migration with LLMs cites this paper.

Qiskit Code Migration with LLMs Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-06-26T16:29:35.520168Z

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=2026-06-26T16:24:25.357338Z digest=sha256:fd4a2c1e1fd41e1dc93005d9f552129965b6556ce623c0ad84a74e12b66e02db

Observation fa765d41-fe23-4f25-96b0-f5887e7e8317 · inbound

Quantize with Confidence? An Empirical Study of Quantization for Code Generation cites this paper.

Quantize with Confidence? An Empirical Study of Quantization for Code Generation Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-02T03:32:48.904884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T03:32:48.904884Z digest=sha256:60565812ae2ef037930ef19ba9280e2e4cc654ce6528ed5437b1e62277c0407b

Observation 76c3128b-b8f6-491f-9044-1975539ba6d2 · inbound

Large Language Models for Code Generation from Multilingual Prompts: A Curated Benchmark and a Study on Code Quality cites this paper.

Large Language Models for Code Generation from Multilingual Prompts: A Curated Benchmark and a Study on Code Quality Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 105

Resolution
unresolved
no resolver link, observed 2026-08-02T01:01:33.546349Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:01:33.546349Z digest=sha256:d8b4f437d0c7ac84fe98d99269bee3270e0efcadc61e6e9445593655d7414142

Observation 37fff69e-d8b3-448c-b348-27515ea2e447 · inbound

Design Theater: Evaluating the Gap Between User-Facing Design Reasoning and Implementation in Generative UI Tools cites this paper.

Design Theater: Evaluating the Gap Between User-Facing Design Reasoning and Implementation in Generative UI Tools Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-01T04:11:14.979264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T04:11:14.979264Z digest=sha256:ad3a48de96caa21f15cf33d0c9190909d8516102525eee8b3df069cc9e196aa1

Observation 6769482c-be19-4780-91d9-39d203be9dd1 · inbound

Design Theater: Evaluating the Gap Between User-Facing Design Reasoning and Implementation in Generative UI Tools cites this paper.

Design Theater: Evaluating the Gap Between User-Facing Design Reasoning and Implementation in Generative UI Tools Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 41

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no resolver link, observed 2026-08-04T01:35:39.591608Z

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source=arxiv_source observed=2026-08-04T01:35:39.591608Z digest=sha256:df5798b898b2df5cb16d8c00b821b11bc445ca98ed65babe4ec26072126e8070