Paper Citation Record · LEDGER

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study

As of 18 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 4 inbound Pith citation observations for arXiv:2412.18989.

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

pith.paper-citation-record.v1
2412.18989 v2

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T01:04:03.644079Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:17:35.374595Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T17:21:10.908503Z

Reference resolution

45 of 45 outbound references displayed

verified exact4
verified fuzzy32
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8c8e02bf-26b7-4949-a787-60feb4091439 · outbound

This paper cites An empirical study on the usage of transformer models for code completion,.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study An empirical study on the usage of transformer models for code completion,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:04.663359Z

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Observation 8e557a6a-917a-4907-9809-e78844a645d6 · outbound

This paper cites SOEN-101: Code Generation by Emulating Software Process Models Using Large Language Model Agents.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study SOEN-101: Code Generation by Emulating Software Process Models Using Large Language Model Agents

Reference 2

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Observation 3708045c-766f-4a85-9052-c70a950cc1c6 · outbound

This paper cites Toward deep learning software reposi- tories,.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study Toward deep learning software reposi- tories,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:04.646725Z

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Observation 3a94e3f5-60d1-4571-97a2-eb2b061cd0d6 · outbound

This paper cites Few-shot training llms for project-specific code-summarization,.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study Few-shot training llms for project-specific code-summarization,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:04.631605Z

Source-reported events for the cited work

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Observation 49064042-8472-40b9-98c2-5b66006880b9 · outbound

This paper cites Inferfix: End-to-end program repair with llms,.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study Inferfix: End-to-end program repair with llms,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:04.610995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1feb2ec2-6ea3-41c4-99e9-b77bd00cb82c · outbound

This paper cites Deep learning code fragments for code clone detection,.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study Deep learning code fragments for code clone detection,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:04.591439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 25084cca-18a8-4ac4-81ed-8bd501399e17 · outbound

This paper cites On learning meaningful assert statements for unit test cases,.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study On learning meaningful assert statements for unit test cases,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:04.572962Z

Source-reported events for the cited work

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Observation bf8022e6-97f5-4fd6-b529-bb16756d5eb4 · outbound

This paper cites A systematic literature review on the use of deep learning in software engineering research,.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study A systematic literature review on the use of deep learning in software engineering research,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:04.557003Z

Source-reported events for the cited work

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Observation d9daa52d-26e9-4ff6-969f-92aa962e5e51 · outbound

This paper cites When and Why Your Code Starts to Smell Bad (and Whether the Smells Go Away),.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study When and Why Your Code Starts to Smell Bad (and Whether the Smells Go Away),

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:04.538147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1d358ee4-d47a-49a8-8f7e-611103c185d6 · outbound

This paper cites On the diffuseness and the impact on maintainability of code smells: a large scale empirical investigation,.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study On the diffuseness and the impact on maintainability of code smells: a large scale empirical investigation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:04.518728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 08cafb2d-6691-48e3-8983-fca985485e3f · outbound

This paper cites Vulnerability Handling of AI-Generated Code -- Existing Solutions and Open Challenges.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study Vulnerability Handling of AI-Generated Code -- Existing Solutions and Open Challenges

Reference 11

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Source-reported events for the cited work

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Observation 9beb1c46-93eb-45af-ba9b-eb21d3080a0c · outbound

This paper cites CodeLMSec benchmark: Systematically evaluating and finding security vulnerabilities in black-box code lan- guage models,.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study CodeLMSec benchmark: Systematically evaluating and finding security vulnerabilities in black-box code lan- guage models,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:04.499930Z

Source-reported events for the cited work

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Observation 604cae6f-c46c-4e2e-ab6e-d00da5a12fac · outbound

This paper cites SALLM: Security Assessment of Generated Code.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study SALLM: Security Assessment of Generated Code

Reference 13

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Source-reported events for the cited work

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Observation d75ffbc4-0343-4067-b2aa-57b852050647 · outbound

This paper cites DLAP: A Deep Learning Augmented Large Language Model Prompting Framework for Software Vulnerability Detection.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study DLAP: A Deep Learning Augmented Large Language Model Prompting Framework for Software Vulnerability Detection

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-11T01:04:03.985672Z

Source-reported events for the cited work

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Observation 616c364a-45f6-4e45-a6e0-8660d05b530d · outbound

This paper cites Evaluating Large Language Models in Detecting Test Smells.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study Evaluating Large Language Models in Detecting Test Smells

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-11T01:04:03.960809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 88b79e18-58ca-4138-9e1e-ea1cbd336ac1 · outbound

This paper cites Code smell detection using hy- brid machine learning algorithms,.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study Code smell detection using hy- brid machine learning algorithms,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:04.478649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fcebad28-2922-4413-a8d3-c80377a9a3ee · outbound

This paper cites Multi-Label Code Smell Detection with Hybrid Model based on Deep Learning,.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study Multi-Label Code Smell Detection with Hybrid Model based on Deep Learning,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:04.459092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation afc4a3f8-d920-4833-a499-935fc97b243e · outbound

This paper cites iSMELL: Assembling LLMs with Expert Toolsets for Code Smell Detection and Refactoring,.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study iSMELL: Assembling LLMs with Expert Toolsets for Code Smell Detection and Refactoring,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:04.438375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 872550b3-bded-4f1f-8a79-fee044d9b43f · outbound

This paper cites BLEU: a method for automatic evaluation of machine translation,.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study BLEU: a method for automatic evaluation of machine translation,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:04.417579Z

Source-reported events for the cited work

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Observation 31fd5b27-340c-49fd-87e2-4660d7410cbd · outbound

This paper cites CodeBLEU: a Method for Automatic Evaluation of Code Synthesis.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study CodeBLEU: a Method for Automatic Evaluation of Code Synthesis

Reference 20

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Source-reported events for the cited work

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Observation 535866fd-a9b0-41fc-807f-5ff433ee9696 · outbound

This paper cites ROUGE: A Package for Automatic Evaluation of Sum- maries,.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study ROUGE: A Package for Automatic Evaluation of Sum- maries,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:04.400047Z

Source-reported events for the cited work

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Observation e8b9eec1-90af-4a1c-b751-d4012a64dd18 · outbound

This paper cites Meteor: an automatic metric for mt evaluation with high levels of correlation with human judgments,.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study Meteor: an automatic metric for mt evaluation with high levels of correlation with human judgments,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:04.380061Z

Source-reported events for the cited work

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Observation d58e88c7-0c18-41e5-9ac3-51fec2e21e34 · outbound

This paper cites A systematic literature review on the use of deep learning in software engineering research,.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study A systematic literature review on the use of deep learning in software engineering research,

Reference 23

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Source-reported events for the cited work

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Observation f91b7c1d-0f8a-463d-b121-c915bd08c090 · outbound

This paper cites Towards More Trust- worthy and Interpretable LLMs for Code through Syntax-Grounded Explanations,.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study Towards More Trust- worthy and Interpretable LLMs for Code through Syntax-Grounded Explanations,

Reference 24

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Observation c6271d76-e3a4-4208-b32b-886d0b184242 · outbound

This paper cites Which syntactic capabilities are statistically learned by masked language models for code?.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study Which syntactic capabilities are statistically learned by masked language models for code?

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:04.341291Z

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Observation d843fc25-c085-40c5-a4bb-2e7e0b3b0dae · outbound

This paper cites Codesmells,.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study Codesmells,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:04.322889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8dcf6c74-f5d1-493f-9e93-defc121d301c · outbound

This paper cites Visualizing and Understanding Recurrent Networks.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study Visualizing and Understanding Recurrent Networks

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T01:04:03.533227Z

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Observation 53a9e426-9a90-445d-872d-308cadaa0d49 · outbound

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How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study Unresolved cited work

Reference 28

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Observation 59a871b3-bfc3-46ff-82a7-213bc33082dd · outbound

This paper cites Benchmarking causal study to interpret large language models for source code,.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study Benchmarking causal study to interpret large language models for source code,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:04.274862Z

Source-reported events for the cited work

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Observation 75a4ba42-b2fc-46b5-b3e8-8a138d00d3b3 · outbound

This paper cites Code Llama: Open Foundation Models for Code.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study Code Llama: Open Foundation Models for Code

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T01:04:03.549799Z

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Observation f8e76506-26e5-45db-bf6d-e992454b5e6d · outbound

This paper cites Mistral 7B.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study Mistral 7B

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T01:04:03.556840Z

Source-reported events for the cited work

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Observation 7b08d064-358f-46ec-92d7-ce1252d348c2 · outbound

This paper cites Code smell detection using hy- brid machine learning algorithms,.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study Code smell detection using hy- brid machine learning algorithms,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:04.256271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6c9b0ac3-f9b6-4aa7-8564-e1f76c1ae49b · outbound

This paper cites Machine learning powered code smell de- tection as a business improvement tool,.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study Machine learning powered code smell de- tection as a business improvement tool,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:04.239837Z

Source-reported events for the cited work

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Observation c1e00833-c93b-4aa2-b548-6e61aa40d5f3 · outbound

This paper cites Can We Trust Large Language Models Generated Code? A Framework for In-Context Learning, Security Patterns, and Code Evaluations Across Diverse LLMs.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study Can We Trust Large Language Models Generated Code? A Framework for In-Context Learning, Security Patterns, and Code Evaluations Across Diverse LLMs

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T01:04:03.574583Z

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Unavailable: canonical work link unavailable.

Observation b746481d-dcd9-4cf4-8980-9f7b8922378e · outbound

This paper cites A Systematic Literature Review on the Code Smells Datasets and Validation Mechanisms,.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study A Systematic Literature Review on the Code Smells Datasets and Validation Mechanisms,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:04.222541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 66a55f9b-506f-41dd-a668-36d8ef0acdc9 · outbound

This paper cites ml-Codesmell: A code smell prediction dataset for machine learning approaches,.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study ml-Codesmell: A code smell prediction dataset for machine learning approaches,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:04.204730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 912c9ca7-7a9b-4cf9-b82d-db08d4c33db1 · outbound

This paper cites Mlcq: Industry-relevant code smell data set,.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study Mlcq: Industry-relevant code smell data set,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:04.187375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2538c857-0072-43b6-91b9-45b9acff69f0 · outbound

This paper cites Evaluating the accuracy of machine learning algorithms on detecting code smells for different developers,.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study Evaluating the accuracy of machine learning algorithms on detecting code smells for different developers,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:04.171338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fcb823cf-1ffc-4338-a32d-292805d193a8 · outbound

This paper cites DACOS—a manually annotated dataset of code smells,.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study DACOS—a manually annotated dataset of code smells,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:04.153186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 912d416b-9b36-40d1-a474-3e8579c501c2 · outbound

This paper cites The Technical Debt Dataset,.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study The Technical Debt Dataset,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:04.129712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 69b60e96-dbfd-4169-8031-fa9fb4039542 · outbound

This paper cites Using code evolution information to improve the quality of labels in code smell datasets,.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study Using code evolution information to improve the quality of labels in code smell datasets,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:04.110103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8b65d380-44ba-4d29-8582-1a4e1e7ec806 · outbound

This paper cites Prompt Learning for Multi-Label Code Smell Detection: A Promising Approach.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study Prompt Learning for Multi-Label Code Smell Detection: A Promising Approach

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-08-11T01:04:03.704852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 944f827f-5e2a-4dfb-bb44-64a276c1f7d0 · outbound

This paper cites Toward a theory of causation for interpreting neural code models,.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study Toward a theory of causation for interpreting neural code models,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:04.093037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1ae8623c-9850-465a-ad51-b3071431b412 · outbound

This paper cites ”why should i trust you?.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study ”why should i trust you?

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:04.076285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0ca3de16-dcf5-4eba-9bfa-cba00155576b · outbound

This paper cites A unified approach to interpreting model predictions,.

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study A unified approach to interpreting model predictions,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:04:04.059394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

Observation 9e665133-d85c-4869-ae5b-c4ded1d91d73 · inbound

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency cites this paper.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study

Reference 30

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Observation c8d06b7e-9688-49bb-bc04-b9eb0b08fdf5 · inbound

Benchmarking LLM for Code Smells Detection: OpenAI GPT-4.0 vs DeepSeek-V3 cites this paper.

Benchmarking LLM for Code Smells Detection: OpenAI GPT-4.0 vs DeepSeek-V3 How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study

Reference 4

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Observation 75937992-3c33-4279-abbe-2724dc63252a · inbound

A Causal Perspective on Measuring, Explaining and Mitigating Smells in LLM-Generated Code cites this paper.

A Causal Perspective on Measuring, Explaining and Mitigating Smells in LLM-Generated Code How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-04T06:49:10.487496Z

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Observation c4be3898-66d8-4c11-9a73-d9fa90977d3d · 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 How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study

Reference 123

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:21:10.911009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.