From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models | Paper Citation Record

Observation 0915a576-d42f-4f4f-bd8c-1f37bf65ad24 · outbound

This paper cites chatgpt, howpublished = https://chat.openai.com/, note = Accessed: 2023-09-23.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models chatgpt, howpublished = https://chat.openai.com/, note = Accessed: 2023-09-23

Reference 1

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From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Unresolved cited work

Reference 2

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From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Unresolved cited work

Reference 3

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This paper cites OpenAI-models, howpublished = https://platform.openai.com/docs/models/gpt-3-5, note = Accessed: 2023-09-23.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models OpenAI-models, howpublished = https://platform.openai.com/docs/models/gpt-3-5, note = Accessed: 2023-09-23

Reference 4

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This paper cites PoolC-5-fold-clone-detection-600k-5fold, howpublished = https://huggingface.co/datasets/poolc/5-fold-clone- detection-600k-5fold, note = Accessed: 2023-09-23.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models PoolC-5-fold-clone-detection-600k-5fold, howpublished = https://huggingface.co/datasets/poolc/5-fold-clone- detection-600k-5fold, note = Accessed: 2023-09-23

Reference 5

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This paper cites surveyMonkey-sample size calculator, howpublished = https://www.surveymonkey.com/mp/sample-size-calculator/, note = Accessed: 2023-09-23.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models surveyMonkey-sample size calculator, howpublished = https://www.surveymonkey.com/mp/sample-size-calculator/, note = Accessed: 2023-09-23

Reference 6

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This paper cites wasiahmad-AVATAR, howpublished = https://github.com/wasiahmad/avatar, note = Accessed: 2023-09-23.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models wasiahmad-AVATAR, howpublished = https://github.com/wasiahmad/avatar, note = Accessed: 2023-09-23

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.979171Z

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This paper cites Black box fairness testing of machine learning models,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Black box fairness testing of machine learning models,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.967008Z

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This paper cites A systematic review on code clone detection,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models A systematic review on code clone detection,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.951928Z

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This paper cites Artificial hallucinations in chatgpt: implications in scientific writing,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Artificial hallucinations in chatgpt: implications in scientific writing,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.934606Z

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This paper cites The adverse effects of code duplication in machine learning models of code,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models The adverse effects of code duplication in machine learning models of code,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.923784Z

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This paper cites Clone detection using abstract syntax trees,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Clone detection using abstract syntax trees,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.913340Z

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This paper cites Do the machine learning models on a crowd sourced platform exhibit bias? an empirical study on model fairness,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Do the machine learning models on a crowd sourced platform exhibit bias? an empirical study on model fairness,

Reference 13

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This paper cites Multiple significance tests: the bonferroni method,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Multiple significance tests: the bonferroni method,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.892268Z

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This paper cites Making fair ml software using trustworthy explanation,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Making fair ml software using trustworthy explanation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.882169Z

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This paper cites Software Engineering for Fairness: A Case Study with Hyperparameter Optimization.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Software Engineering for Fairness: A Case Study with Hyperparameter Optimization

Reference 16

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This paper cites PaLM: Scaling Language Modeling with Pathways.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models PaLM: Scaling Language Modeling with Pathways

Reference 17

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This paper cites Sample size determination,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Sample size determination,

Reference 18

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This paper cites A language independent approach for detecting duplicated code,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models A language independent approach for detecting duplicated code,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.860066Z

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This paper cites Functional code clone detection with syntax and semantics fusion learning,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Functional code clone detection with syntax and semantics fusion learning,

Reference 20

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This paper cites CodeBERT: A Pre-Trained Model for Programming and Natural Languages.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models CodeBERT: A Pre-Trained Model for Programming and Natural Languages

Reference 21

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This paper cites An empirical examination of the impact of bias on just-in-time defect prediction,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models An empirical examination of the impact of bias on just-in-time defect prediction,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.848044Z

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From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models News Summarization and Evaluation in the Era of GPT-3

Reference 23

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This paper cites Bertopic: Neural topic modeling with a class-based tf-idf procedure,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Bertopic: Neural topic modeling with a class-based tf-idf procedure,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.835138Z

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This paper cites Computing inter-rater reliability and its variance in the presence of high agreement,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Computing inter-rater reliability and its variance in the presence of high agreement,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.824612Z

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This paper cites The fickle p value generates irreproducible results,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models The fickle p value generates irreproducible results,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.814693Z

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This paper cites Is neuron coverage a meaningful measure for testing deep neural networks?.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Is neuron coverage a meaningful measure for testing deep neural networks?

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.803476Z

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This paper cites How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation

Reference 28

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This paper cites How Secure is Code Generated by ChatGPT?.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models How Secure is Code Generated by ChatGPT?

Reference 29

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This paper cites T test as a parametric statistic,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models T test as a parametric statistic,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.792539Z

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This paper cites Using slicing to identify duplication in source code,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Using slicing to identify duplication in source code,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.781232Z

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This paper cites Identifying similar code with program dependence graphs,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Identifying similar code with program dependence graphs,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.770803Z

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This paper cites Bigclonebench considered harmful for machine learning,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Bigclonebench considered harmful for machine learning,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.760625Z

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From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models A mathematical investigation of hallucination and creativity in gpt models,

Reference 34

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This paper cites Clorifi: software vulnerability discovery using code clone verification,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Clorifi: software vulnerability discovery using code clone verification,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.736462Z

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This paper cites Improving ChatGPT Prompt for Code Generation.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Improving ChatGPT Prompt for Code Generation

Reference 36

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This paper cites Refining ChatGPT-Generated Code: Characterizing and Mitigating Code Quality Issues.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Refining ChatGPT-Generated Code: Characterizing and Mitigating Code Quality Issues

Reference 37

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This paper cites Déjàvu: a map of code duplicates on github,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Déjàvu: a map of code duplicates on github,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.724493Z

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This paper cites hdbscan: Hierarchical density based clustering.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models hdbscan: Hierarchical density based clustering

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.712564Z

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This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 40

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This paper cites Ablation Studies in Artificial Neural Networks.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Ablation Studies in Artificial Neural Networks

Reference 41

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This paper cites Training language models to follow instructions with human feedback,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Training language models to follow instructions with human feedback,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.700416Z

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This paper cites Language models are unsupervised multitask learners,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Language models are unsupervised multitask learners,

Reference 43

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This paper cites Toxic code snippets on stack overflow,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Toxic code snippets on stack overflow,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.684438Z

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Observation afe2bf3d-f605-4349-8d3e-07c10b35e817 · outbound

This paper cites Comparison and evaluation of code clone detection techniques and tools: A qualitative approach,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Comparison and evaluation of code clone detection techniques and tools: A qualitative approach,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.675013Z

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Observation 4251fd88-9bf6-49db-be96-7edaffc2dc6f · outbound

This paper cites A survey on software clone detection research,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models A survey on software clone detection research,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.664729Z

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Observation ae716d18-6584-4042-a505-7d7f64146d44 · outbound

This paper cites Code clones: Detection and management,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Code clones: Detection and management,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.654471Z

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Observation 32ee2f81-cdde-4a32-a53b-d83cc74f627e · outbound

This paper cites Generalizability of code clone detection on codebert,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Generalizability of code clone detection on codebert,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.643935Z

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Observation 05c80c00-eac1-4204-b03e-afc3ee9891ed · outbound

This paper cites Basics of qualitative research techniques,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Basics of qualitative research techniques,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.632868Z

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Observation b1915ed3-2fe2-4f4e-adc4-a784d4ce1410 · outbound

This paper cites Code clone detection based on order and content of control statements,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Code clone detection based on order and content of control statements,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.622229Z

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Observation 8cadd391-3980-431b-8b9a-4583784bbb32 · outbound

This paper cites Testing dnn image classifiers for confusion & bias errors,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Testing dnn image classifiers for confusion & bias errors,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.611705Z

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Observation 58ce272c-4b2a-4108-99ee-06b0c7112c8d · outbound

This paper cites Detecting code clones with graph neural network and flow-augmented abstract syntax tree,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Detecting code clones with graph neural network and flow-augmented abstract syntax tree,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.601193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8ae86f8b-de6a-4ad8-8ff1-9818f3ce7ef3 · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-15T23:05:17.235983Z

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Observation 37bace6b-1a67-40cf-a11f-30550e06e400 · outbound

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

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-15T23:05:17.239660Z

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Observation c00b2b10-8c1e-40e3-b34e-70c67a25c7cc · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Chain-of-thought prompting elicits reasoning in large language models,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.590975Z

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Observation f433a4aa-9b54-49d1-b740-1b9e824c4f96 · outbound

This paper cites DomBERT: Domain-oriented Language Model for Aspect-based Sentiment Analysis.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models DomBERT: Domain-oriented Language Model for Aspect-based Sentiment Analysis

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:05:17.321704Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1374a043-45a1-4b4f-9a23-3f907da7007b · outbound

This paper cites Graph-based code semantics learning for efficient semantic code clone detection,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Graph-based code semantics learning for efficient semantic code clone detection,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.580294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cbcbbc4f-1dc0-43c0-a6bd-86ceba48e718 · outbound

This paper cites Cloning practices: Why developers clone and what can be changed,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Cloning practices: Why developers clone and what can be changed,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.569766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 86bd7f8f-67a0-44a7-bb57-69d7854b9f89 · outbound

This paper cites A survey of software clone detection from security perspective,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models A survey of software clone detection from security perspective,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.559141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ef2ad2cc-fc25-49cf-9d5a-bd966c346a0a · outbound

This paper cites Sentiment analysis for software engineering: How far can pre-trained transformer models go?.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Sentiment analysis for software engineering: How far can pre-trained transformer models go?

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.547812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1fd16edd-236a-47c7-9131-5107c25d2f46 · outbound

This paper cites Deepsim: deep learning code functional similarity,.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Deepsim: deep learning code functional similarity,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:05:17.536460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 603f7215-67c8-4da4-948e-1a68f745314e · outbound

This paper cites Large Language Models Are Human-Level Prompt Engineers.

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models Large Language Models Are Human-Level Prompt Engineers

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-15T23:05:17.269772Z

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