Paper Citation Record · LEDGER

A Large Language Model Approach to Identify Flakiness in C++ Projects

As of 16 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2412.12340.

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

pith.paper-citation-record.v1
2412.12340 v2

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:13:50.047565Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

43 of 43 outbound references displayed

verified exact0
verified fuzzy32
unresolved11
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 45b16767-f076-434a-9ec4-3e411e2541c5 · outbound

This paper cites Taming timeout flakiness: An empirical study of sap hana,.

A Large Language Model Approach to Identify Flakiness in C++ Projects Taming timeout flakiness: An empirical study of sap hana,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.508934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c0d809a6-b80e-4e73-a624-fe3ee458160a · outbound

This paper cites Software testing research challenges: An industrial perspective,.

A Large Language Model Approach to Identify Flakiness in C++ Projects Software testing research challenges: An industrial perspective,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.499099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a62b35c6-db09-43ca-b7b3-a61e5621335b · outbound

This paper cites Deflaker: Automatically detecting flaky tests,.

A Large Language Model Approach to Identify Flakiness in C++ Projects Deflaker: Automatically detecting flaky tests,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.489270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5766d0c8-aab0-46f1-b673-32ac286bc1ab · outbound

This paper cites De-flake your tests: Automatically locating root causes of flaky tests in code at google,.

A Large Language Model Approach to Identify Flakiness in C++ Projects De-flake your tests: Automatically locating root causes of flaky tests in code at google,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.477829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d607fe4a-9f41-41a6-a5a6-756e53ec8ed2 · outbound

This paper cites What do developer -repaired flaky tests tell us about the effective ness of automated flaky test detection?,.

A Large Language Model Approach to Identify Flakiness in C++ Projects What do developer -repaired flaky tests tell us about the effective ness of automated flaky test detection?,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.467699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ab32545d-9088-41f0-aa32-1caf22788bff · outbound

This paper cites A multi -factor approach for flaky test detection and automated root cause analysis,.

A Large Language Model Approach to Identify Flakiness in C++ Projects A multi -factor approach for flaky test detection and automated root cause analysis,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.456493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8375aef0-ecad-423c-bdec-1c9a6dd96cb9 · outbound

This paper cites Flakycat: predicting flaky tests categories using few-shot learning,.

A Large Language Model Approach to Identify Flakiness in C++ Projects Flakycat: predicting flaky tests categories using few-shot learning,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.446361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 06135752-7c71-4c19-99d5-37d8c9763fe2 · outbound

This paper cites Continuous practices and devops: beyond the buzz, what does it all mean?,.

A Large Language Model Approach to Identify Flakiness in C++ Projects Continuous practices and devops: beyond the buzz, what does it all mean?,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.435043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1127c986-34b3-4215-99d1-c5fc443c64af · outbound

This paper cites A qualitative study on the sources, impacts, and mitigation strategies of fla ky tests,.

A Large Language Model Approach to Identify Flakiness in C++ Projects A qualitative study on the sources, impacts, and mitigation strategies of fla ky tests,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.425056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a51adb9a-fd34-417b-bd79-42750da0096e · outbound

This paper cites Understanding flaky tests: The developer’s perspective,.

A Large Language Model Approach to Identify Flakiness in C++ Projects Understanding flaky tests: The developer’s perspective,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.416018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7c1af11d-cc88-42dd-8d1a-87589ae24624 · outbound

This paper cites Taming google-scale continuous testing,.

A Large Language Model Approach to Identify Flakiness in C++ Projects Taming google-scale continuous testing,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.405925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 27c70ad6-15cb-4781-a947-c669f23a5356 · outbound

This paper cites Root causing flaky tests in a large-scale industrial setting,.

A Large Language Model Approach to Identify Flakiness in C++ Projects Root causing flaky tests in a large-scale industrial setting,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.395517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1786ba43-1bf8-4190-bc97-46b3b3e47ea5 · outbound

This paper cites A survey of flaky tests,.

A Large Language Model Approach to Identify Flakiness in C++ Projects A survey of flaky tests,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.385307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ec6066da-b138-4f39-846f-a549c671ec83 · outbound

This paper cites A survey on how test flakiness affects developers and what support they need to address it,.

A Large Language Model Approach to Identify Flakiness in C++ Projects A survey on how test flakiness affects developers and what support they need to address it,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.375335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a4b849d9-054d-4551-bd83-c876bd668b65 · outbound

This paper cites Shake it! detecting flaky tests caused by concurrency with shaker,.

A Large Language Model Approach to Identify Flakiness in C++ Projects Shake it! detecting flaky tests caused by concurrency with shaker,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.365334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c218e360-22e3-4bf3-8562-4169f4527a8f · outbound

This paper cites iDFlakies: A framework for detecting and partially classifying flaky tests,.

A Large Language Model Approach to Identify Flakiness in C++ Projects iDFlakies: A framework for detecting and partially classifying flaky tests,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.356037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 46b6a9cf-8930-45b9-987b-eb2f2fa1b8fc · outbound

This paper cites Empirically revisiting the test independence assumption,.

A Large Language Model Approach to Identify Flakiness in C++ Projects Empirically revisiting the test independence assumption,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.346865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d8a6e5cd-7f42-4a1f-b039-227ad8686dd4 · outbound

This paper cites What is the vocabulary of flaky tests?,.

A Large Language Model Approach to Identify Flakiness in C++ Projects What is the vocabulary of flaky tests?,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.336280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f81427e8-065a-488f-950a-4cd5544b9637 · outbound

This paper cites Towards a bayesian netwo rk model for predicting flaky automated tests,.

A Large Language Model Approach to Identify Flakiness in C++ Projects Towards a bayesian netwo rk model for predicting flaky automated tests,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.324869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 09abe0ad-d71f-41cf-8965-3b5635506424 · outbound

This paper cites An empirical analysis of flaky tests,.

A Large Language Model Approach to Identify Flakiness in C++ Projects An empirical analysis of flaky tests,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.314387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9338c079-fbdd-4c1a-a5dc-299b6813bf81 · outbound

This paper cites An empirical study of c++ vulnerabilities in crowd-sourced code examples,.

A Large Language Model Approach to Identify Flakiness in C++ Projects An empirical study of c++ vulnerabilities in crowd-sourced code examples,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.303700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0a67cb4f-1f1b-4f14-bc48-88df992344d9 · outbound

This paper cites Enhancing Large Language Models for Text-to-Testcase Generation.

A Large Language Model Approach to Identify Flakiness in C++ Projects Enhancing Large Language Models for Text-to-Testcase Generation

Reference 22

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Source-reported events for the cited work

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Observation 403549ef-fa3e-46ba-8fa3-54e3108cd659 · outbound

This paper cites Summary of chatgpt-related research and perspective towards the future of large language models,.

A Large Language Model Approach to Identify Flakiness in C++ Projects Summary of chatgpt-related research and perspective towards the future of large language models,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.292354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 299bc430-50f5-4228-ad4c-2e2d3dac5ff8 · outbound

This paper cites CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation.

A Large Language Model Approach to Identify Flakiness in C++ Projects CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T14:13:49.981168Z

Source-reported events for the cited work

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Observation 144715c7-5b5e-42ab-bc4c-6c8b21b9ac17 · outbound

This paper cites No More Manual Tests? Evaluating and Improving ChatGPT for Unit Test Generation.

A Large Language Model Approach to Identify Flakiness in C++ Projects No More Manual Tests? Evaluating and Improving ChatGPT for Unit Test Generation

Reference 25

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Source-reported events for the cited work

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Observation 0ef48385-1839-4a22-a03a-7d700d491f2b · outbound

This paper cites ChatUniTest: A Framework for LLM-Based Test Generation.

A Large Language Model Approach to Identify Flakiness in C++ Projects ChatUniTest: A Framework for LLM-Based Test Generation

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T14:13:49.988856Z

Source-reported events for the cited work

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Observation 684428c1-f988-468e-a094-7058091f2c39 · outbound

This paper cites Chapter 7 - learning to weight similarity measures with siamese networks: a case study on optimum -path forest,.

A Large Language Model Approach to Identify Flakiness in C++ Projects Chapter 7 - learning to weight similarity measures with siamese networks: a case study on optimum -path forest,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.281512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e8a7968e-a5bb-4ef5-8e77-2ce6b29597d3 · outbound

This paper cites FlakyFix: Using Large Language Models for Predicting Flaky Test Fix Categories and Test Code Repair.

A Large Language Model Approach to Identify Flakiness in C++ Projects FlakyFix: Using Large Language Models for Predicting Flaky Test Fix Categories and Test Code Repair

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T14:13:49.996288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2fd3160c-bf78-4799-8ff3-9663bb5b5c17 · outbound

This paper cites Data augmentation using pre -trained transformer models,.

A Large Language Model Approach to Identify Flakiness in C++ Projects Data augmentation using pre -trained transformer models,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.271377Z

Source-reported events for the cited work

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Observation bab2dad4-fe8d-4db2-89bc-553a73d20460 · outbound

This paper cites Learning data manipulation for augmentation and weighting,.

A Large Language Model Approach to Identify Flakiness in C++ Projects Learning data manipulation for augmentation and weighting,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.260845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 754f6750-1714-4650-9577-72c4c260746b · outbound

This paper cites GPT-4 Technical Report.

A Large Language Model Approach to Identify Flakiness in C++ Projects GPT-4 Technical Report

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T14:13:50.006935Z

Source-reported events for the cited work

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Observation b49edf38-18a6-4539-8eaf-14bd5e773016 · outbound

This paper cites Smote: synthetic minority oversampling technique,.

A Large Language Model Approach to Identify Flakiness in C++ Projects Smote: synthetic minority oversampling technique,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.250455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d55ff7b2-43bf-402a-baf2-e50d9704b5d3 · outbound

This paper cites Test flakiness across programming languages,.

A Large Language Model Approach to Identify Flakiness in C++ Projects Test flakiness across programming languages,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.239671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation dc1fa1ec-9ff2-4c4e-baa9-a2d81d69454a · outbound

This paper cites What made this test flake? pinpointing classes responsible for test flakiness,.

A Large Language Model Approach to Identify Flakiness in C++ Projects What made this test flake? pinpointing classes responsible for test flakiness,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.228199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 51942a95-27e3-4cff-b3de-ea5b5bf2e8c5 · outbound

This paper cites ifixflakies: a framework for automatically fixing order-dependent flaky tests,.

A Large Language Model Approach to Identify Flakiness in C++ Projects ifixflakies: a framework for automatically fixing order-dependent flaky tests,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.217292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 82032150-6fcd-4a65-96f3-ce1859ce36a1 · outbound

This paper cites RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture.

A Large Language Model Approach to Identify Flakiness in C++ Projects RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

Reference 36

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 141880eb-6bd0-4415-9ada-63eed7446f8f · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

A Large Language Model Approach to Identify Flakiness in C++ Projects Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 37

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Source-reported events for the cited work

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Observation 1b770075-94dd-4863-b16e-33d27bf3099c · outbound

This paper cites Mistral 7B.

A Large Language Model Approach to Identify Flakiness in C++ Projects Mistral 7B

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-11T14:13:50.030376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7db73e46-d085-48aa-aaf9-c403fe48853a · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

A Large Language Model Approach to Identify Flakiness in C++ Projects LoRA: Low-Rank Adaptation of Large Language Models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-11T14:13:50.033926Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 458f200b-cead-479c-b64c-75d82ffc857e · outbound

This paper cites Label Supervised LLaMA Finetuning.

A Large Language Model Approach to Identify Flakiness in C++ Projects Label Supervised LLaMA Finetuning

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-11T14:13:50.037263Z

Source-reported events for the cited work

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Observation 7ebf1882-fbd2-4da6-be38-22016180abaf · outbound

This paper cites Scikit-learn: Machine learning in Python,.

A Large Language Model Approach to Identify Flakiness in C++ Projects Scikit-learn: Machine learning in Python,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.205537Z

Source-reported events for the cited work

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Observation c40f4247-6c86-4f92-803b-8a251bd16bae · outbound

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A Large Language Model Approach to Identify Flakiness in C++ Projects Evaluating large language models trained on code,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.194476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ef871e5c-aa09-4a2f-829b-94c9ff729b11 · outbound

This paper cites Fine tuning vs. retrieval augmented generation for less popular knowledge,.

A Large Language Model Approach to Identify Flakiness in C++ Projects Fine tuning vs. retrieval augmented generation for less popular knowledge,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:13:50.184042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.