Paper Citation Record · LEDGER

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study

As of 18 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 3 inbound Pith citation observations for arXiv:2506.07594.

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

pith.paper-citation-record.v1
2506.07594 v1

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:35:41.214905Z

measured 56 of 56 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T16:24:25.357338Z

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

53 of 53 outbound references displayed

verified exact0
verified fuzzy27
unresolved24
parse uncertain0
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External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 23247134-ec7a-462b-b92b-b0da15036b50 · outbound

This paper cites On the relation of test smells to software code quality,.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study On the relation of test smells to software code quality,

Reference 1

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Source-reported events for the cited work

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Observation 8a415b7c-ea53-4703-807f-b2a336d20b4f · outbound

This paper cites On the diffusion of test smells in automatically generated test code: An empirical study,.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study On the diffusion of test smells in automatically generated test code: An empirical study,

Reference 2

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T05:35:42.283784Z

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Observation db152661-eaa6-4851-905d-3fa62b9f4660 · outbound

This paper cites When and why your code starts to smell bad,.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study When and why your code starts to smell bad,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:42.741730Z

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Observation 688e61d6-4cdb-49a5-a2de-f687eabf87dc · outbound

This paper cites An empirical analysis of the distribution of unit test smells and their impact on software maintenance,.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study An empirical analysis of the distribution of unit test smells and their impact on software maintenance,

Reference 4

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Observation 568dc405-c62c-401c-b339-7d00346a4871 · outbound

This paper cites Just-in-time test smell detection and refactoring: The darts project,.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Just-in-time test smell detection and refactoring: The darts project,

Reference 5

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Source-reported events for the cited work

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Observation 1d6ec493-2c87-4666-9895-45d04d52dc64 · outbound

This paper cites GPT-4 Technical Report.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study GPT-4 Technical Report

Reference 6

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Observation 748b762e-2e35-4b29-a32f-bc2578dbe139 · outbound

This paper cites The Llama 3 Herd of Models.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study The Llama 3 Herd of Models

Reference 7

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Observation aea9bf46-fe6a-4274-9c69-d0eb6c0509fe · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 8

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This paper cites Codebert: A pre-trained model for programming and natural languages,.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Codebert: A pre-trained model for programming and natural languages,

Reference 9

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Observation 37b2e45a-2e62-4589-a29e-30195993cc57 · outbound

This paper cites Codexglue: A machine learning benchmark dataset for code understanding and generation,.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Codexglue: A machine learning benchmark dataset for code understanding and generation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:42.692702Z

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This paper cites Top programming languages - the state of the octoverse 2022,.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Top programming languages - the state of the octoverse 2022,

Reference 11

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This paper cites Utilization of pre-trained language model for adapter-based knowledge transfer in software engineering,.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Utilization of pre-trained language model for adapter-based knowledge transfer in software engineering,

Reference 12

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Observation 11ba22fa-d781-4eb2-b61f-2334d7482892 · outbound

This paper cites To- wards efficient fine-tuning of pre-trained code models: An experimental study and beyond,.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study To- wards efficient fine-tuning of pre-trained code models: An experimental study and beyond,

Reference 13

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Observation d2584707-6a9a-40f0-b8f8-2338e8f740ac · outbound

This paper cites An empirical comparison of pre-trained models of source code,.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study An empirical comparison of pre-trained models of source code,

Reference 14

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Observation ab33be99-7a39-4325-9b52-4b05844df9d9 · outbound

This paper cites (2024) Testsmellsrefactoringbyllms.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study (2024) Testsmellsrefactoringbyllms

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:42.629396Z

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Observation abc5f614-3046-456b-a24d-e7157091aecb · outbound

This paper cites Large language models for software engineering: A systematic literature review,.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Large language models for software engineering: A systematic literature review,

Reference 16

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Observation 9007f747-60b9-4a80-9dc6-1896b7c41e46 · outbound

This paper cites Software testing with large language models: Survey, landscape, and vision,.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Software testing with large language models: Survey, landscape, and vision,

Reference 17

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This paper cites An empirical evaluation of using large language models for automated unit test generation,.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study An empirical evaluation of using large language models for automated unit test generation,

Reference 18

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Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Automated test case repair using language models,

Reference 19

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This paper cites Chatunitest: a chatgpt- based automated unit test generation tool,.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Chatunitest: a chatgpt- based automated unit test generation tool,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:42.577039Z

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Observation 7501baa6-8233-41ba-922e-bc3d59d8bb78 · outbound

This paper cites An empirical study of using large language models for unit test generation,.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study An empirical study of using large language models for unit test generation,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:42.561428Z

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Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Towards an understanding of large language models in software engineering tasks,

Reference 22

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This paper cites Pynose: a test smell detector for python,.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Pynose: a test smell detector for python,

Reference 23

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Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Tempy: Test smell detector for python,

Reference 24

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This paper cites Handling test smells in python: Results from a mixed-method study,.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Handling test smells in python: Results from a mixed-method study,

Reference 25

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T05:35:41.834355Z

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Observation 0b467529-eab7-4e27-910f-cd8f9cd3ffd2 · outbound

This paper cites A trend analysis of test smells in python test code over commit history,.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study A trend analysis of test smells in python test code over commit history,

Reference 26

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This paper cites Pytest-smell: A smell detection tool for python unit tests,.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Pytest-smell: A smell detection tool for python unit tests,

Reference 27

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This paper cites A trend analysis of test smells in python test code over commit history,.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study A trend analysis of test smells in python test code over commit history,

Reference 28

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This paper cites Detecting test smells in python test code generated by LLM: an empirical study with github copilot,.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Detecting test smells in python test code generated by LLM: an empirical study with github copilot,

Reference 29

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Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Tsdetect: An open source test smells detection tool,

Reference 30

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This paper cites The secret life of test smells - an empirical study on test smell evolution and maintenance,.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study The secret life of test smells - an empirical study on test smell evolution and maintenance,

Reference 31

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This paper cites An empirical investigation into the nature of test smells,.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study An empirical investigation into the nature of test smells,

Reference 32

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This paper cites An empirical evaluation of raide: A semi-automated approach for test smells detection and refactoring,.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study An empirical evaluation of raide: A semi-automated approach for test smells detection and refactoring,

Reference 33

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Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Machine learning-based test smell detection,

Reference 34

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This paper cites Ml test smell detection - online appendix,.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Ml test smell detection - online appendix,

Reference 35

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Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study The Prompt Report: A Systematic Survey of Prompt Engineering Techniques

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Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT

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Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Finetuned Language Models Are Zero-Shot Learners

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Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Language models are few-shot learners,

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Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

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Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Enhancing zero-shot chain-of-thought reasoning in large language models through logic,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:42.414785Z

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Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Wilcoxon, Individual Comparisons by Ranking Methods

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Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Copilot Evaluation Harness: Evaluating LLM-Guided Software Programming

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Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Towards effective validation and integration of llm-generated code,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:42.399120Z

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This paper cites Challenges and opportunities in integrating llms into con- tinuous integration/continuous deployment (ci/cd) pipelines,.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Challenges and opportunities in integrating llms into con- tinuous integration/continuous deployment (ci/cd) pipelines,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:42.383731Z

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This paper cites Next-generation refactoring: Combining llm insights and ide capabilities for extract method,.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Next-generation refactoring: Combining llm insights and ide capabilities for extract method,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:42.367755Z

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This paper cites Llm-based multi-agent systems for software engineering: Literature review, vision and the road ahead,.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Llm-based multi-agent systems for software engineering: Literature review, vision and the road ahead,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:42.350942Z

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This paper cites Autorefactoring: A platform to build refactoring agents,.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Autorefactoring: A platform to build refactoring agents,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:42.335039Z

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Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study DeepSeek-V3 Technical Report

Reference 49

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This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T05:35:41.204978Z

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This paper cites Runeson, M.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Runeson, M

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:42.318400Z

Source-reported events for the cited work

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Observation 9a477ee6-450c-4e12-8886-ead2dd72cacc · outbound

This paper cites Qualitative methods in empirical studies of software engineering,.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Qualitative methods in empirical studies of software engineering,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:35:42.302511Z

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 a9133213-2008-441f-ab73-53fa2c8f35fb · outbound

This paper cites Language Models are Few-Shot Learners.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study Language Models are Few-Shot Learners

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-07T05:35:41.149159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation a8bbc461-08d7-43e7-a15d-eac1263a4d91 · inbound

An Empirical Evaluation of Locally Deployed LLMs for Bug Detection in Python Code cites this paper.

An Empirical Evaluation of Locally Deployed LLMs for Bug Detection in Python Code Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:46:14.700137Z

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 8902ec67-7c8b-4674-894b-3fbf0d010e3c · inbound

How Compliant Are GitHub Actions Workflows? A Checklist-Based Study with LLM-Assisted Auditing cites this paper.

How Compliant Are GitHub Actions Workflows? A Checklist-Based Study with LLM-Assisted Auditing Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-09T05:55:31.517733Z

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 1fdc2dcf-d38d-430e-9dd7-f7c350880795 · inbound

Qiskit Code Migration with LLMs cites this paper.

Qiskit Code Migration with LLMs Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study

Reference 155

Resolution: verified exact; arxiv_id, observed 2026-06-26T16:29:35.581303Z

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.