From Discussion to Execution: Replicating Buggy and Correct Data Science Code | Paper Citation Record

Observation a3801244-e99a-4587-88af-43b8c64dc68b · outbound

This paper cites Repairing deep neural networks: Fix patterns and challenges,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Repairing deep neural networks: Fix patterns and challenges,

Reference 2

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Observation ec1ffc61-07e8-46fb-87f2-45bc11fb1864 · outbound

This paper cites Characterizing Bugs in Python and R Data Analytics Programs.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Characterizing Bugs in Python and R Data Analytics Programs

Reference 3

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Observation f3b2b4ea-c7be-487c-84e3-101c61eed01b · outbound

This paper cites Towards understanding performance bugs in popular data science libraries,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Towards understanding performance bugs in popular data science libraries,

Reference 4

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Observation 85507504-ba06-476c-bc62-3b5df31bb8f3 · outbound

This paper cites Towards understanding fine-grained programming mistakes and fixing patterns in data science,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Towards understanding fine-grained programming mistakes and fixing patterns in data science,

Reference 5

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Observation e5e22fb2-0db4-4350-8f8f-807fbdec270d · outbound

This paper cites Bug analysis in jupyter notebook projects: An empirical study,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Bug analysis in jupyter notebook projects: An empirical study,

Reference 6

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Observation 22c7e398-bb87-4f7f-a343-0fbdc0bb2b49 · outbound

This paper cites Why do machine learning notebooks crash? an empirical study on public python jupyter notebooks,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Why do machine learning notebooks crash? an empirical study on public python jupyter notebooks,

Reference 7

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Observation a77c61ba-988f-4cd7-9e1b-19dcfb18ef45 · outbound

This paper cites Studying vulnerable code entities in r,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Studying vulnerable code entities in r,

Reference 8

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Observation f84bcaf0-84cf-4bd8-8e79-b206801ba279 · outbound

This paper cites Knowledge-Enhanced Program Repair for Data Science Code.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Knowledge-Enhanced Program Repair for Data Science Code

Reference 9

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Observation c864d44e-5bda-4b52-a3ae-4d9506ffc2ab · outbound

This paper cites Specrover: Code intent extraction via llms,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Specrover: Code intent extraction via llms,

Reference 10

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Observation dcd74a92-7787-4737-8c4b-51ab070af6ec · outbound

This paper cites How effective are llms for data science coding? a controlled experiment,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code How effective are llms for data science coding? a controlled experiment,

Reference 11

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Observation 540bc014-deb2-4a69-9f64-d42364df4bd7 · outbound

This paper cites Improving patch correctness analysis via random testing and large language models,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Improving patch correctness analysis via random testing and large language models,

Reference 12

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Observation b41d7cf1-7797-49ad-9f8d-7ed9b865d258 · outbound

This paper cites Do current language models support code intelligence for r programming language?.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Do current language models support code intelligence for r programming language?

Reference 13

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Observation 7b791086-016f-4091-a4f6-9a3b039b6295 · outbound

This paper cites Can llms replace human evaluators? an empirical study of llm-as-a-judge in software engineering,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Can llms replace human evaluators? an empirical study of llm-as-a-judge in software engineering,

Reference 14

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Observation e3ad60d8-1b80-4c0b-b6c2-bb091f8605f4 · outbound

This paper cites Patch correctness assessment: A survey,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Patch correctness assessment: A survey,

Reference 15

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Observation c7228b25-89a0-4d1d-815e-03d40fbc4cc7 · outbound

This paper cites PatchZero: Zero-Shot Automatic Patch Correctness Assessment.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code PatchZero: Zero-Shot Automatic Patch Correctness Assessment

Reference 16

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Observation 23681fc4-7de1-4198-91a7-8ebdb21ed7ce · outbound

This paper cites Tag Trends Data Science Libraries,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Tag Trends Data Science Libraries,

Reference 17

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Observation cacac818-fa97-44fc-9508-9d42a93aa6e6 · outbound

This paper cites What is the most efficient way of counting occurrences in pandas?.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code What is the most efficient way of counting occurrences in pandas?

Reference 18

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Observation e8fdff6e-da35-4377-8292-91cc118e6840 · outbound

This paper cites Output logs from AutoCodeRover and ArchCode ,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Output logs from AutoCodeRover and ArchCode ,

Reference 19

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Observation 931762d7-2687-468b-bb98-941bc666e30d · outbound

This paper cites Autocoderover: Autonomous program improvement,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Autocoderover: Autonomous program improvement,

Reference 20

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Observation 4d25fb0f-23be-4020-9005-d36d15991ad2 · outbound

This paper cites Archcode: Incor- porating software requirements in code generation with large language 11 models,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Archcode: Incor- porating software requirements in code generation with large language 11 models,

Reference 21

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Observation 84d1835a-ea1a-405d-bf88-71146761950a · outbound

This paper cites ReprodGen Prompts,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code ReprodGen Prompts,

Reference 22

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This paper cites Intermediate artifacts,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Intermediate artifacts,

Reference 23

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Observation 5bbae38e-59ac-4931-be6b-9d3a3c46bbbc · outbound

This paper cites Structured chain-of-thought prompting for code generation,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Structured chain-of-thought prompting for code generation,

Reference 24

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Observation aef7d41c-17c0-43b0-a281-07995ad1c888 · outbound

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

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Chain-of-thought prompting elicits reasoning in large language models,

Reference 25

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Observation 423265e1-4278-4724-bfeb-d5aa0f728ff5 · outbound

This paper cites On reliability of patch correctness assessment,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code On reliability of patch correctness assessment,

Reference 26

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Observation f475e1d1-504f-49b0-b9ac-9be74d0e4de5 · outbound

This paper cites Llm hallucinations in practical code generation: Phenomena, mechanism, and mitigation,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Llm hallucinations in practical code generation: Phenomena, mechanism, and mitigation,

Reference 27

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Observation 0dbd60a6-41ed-4f41-b4d9-d5695fb8b8c7 · outbound

This paper cites Ds patch gen query,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Ds patch gen query,

Reference 28

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Observation 03828dce-ea0b-42a3-b64e-a73827a81edd · outbound

This paper cites Towards ai-assisted synthesis of verified dafny methods,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Towards ai-assisted synthesis of verified dafny methods,

Reference 29

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Observation 4bcd05fb-b035-49e2-888d-e92fe698e1cf · outbound

This paper cites A comprehensive study on deep learning bug characteristics,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code A comprehensive study on deep learning bug characteristics,

Reference 30

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Observation 3daf92f8-9db2-4120-b6b0-0f5c8e19820e · outbound

This paper cites The art and practice of data science pipelines: A comprehensive study of data science pipelines in theory, in-the-small, and in-the-large,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code The art and practice of data science pipelines: A comprehensive study of data science pipelines in theory, in-the-small, and in-the-large,

Reference 31

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Observation 49ecd25a-899f-41d5-ad08-4ccdb02c7e03 · outbound

This paper cites Bigcode models leaderboard,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Bigcode models leaderboard,

Reference 32

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Observation 4ea1952e-bfdc-48a9-b4d6-4c8bf869d6aa · outbound

This paper cites Qwen3 Technical Report.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Qwen3 Technical Report

Reference 33

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Observation 936b7e89-cf60-463e-9139-c376623c820a · outbound

This paper cites Gemma 3 Technical Report.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Gemma 3 Technical Report

Reference 34

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Observation c435b79e-45cb-496a-82d3-fed828675a4c · outbound

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From Discussion to Execution: Replicating Buggy and Correct Data Science Code Llama 3: Open and efficient foundation language models,

Reference 35

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Observation 33003dfc-5152-489f-9bcb-0c8d63e7c98d · outbound

This paper cites DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence

Reference 36

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Observation 422a8c36-adc6-4b0b-9e1a-32cc912daf89 · outbound

This paper cites Phi-4 Technical Report.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Phi-4 Technical Report

Reference 37

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Observation 0ba18a7b-e203-481a-8929-714ca70f00b8 · outbound

This paper cites Evaluating and improving chatgpt for unit test generation,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Evaluating and improving chatgpt for unit test generation,

Reference 38

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Observation 9f42ef89-7bf6-4e0d-a923-8761c82f2cc6 · outbound

This paper cites Jailbreakbench: An open robustness benchmark for jailbreaking large language models,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Jailbreakbench: An open robustness benchmark for jailbreaking large language models,

Reference 39

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Observation c58ed821-6f0d-424a-a467-a05849cb90f2 · outbound

This paper cites CodeBERTScore: Evaluating code generation with pretrained models of code,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code CodeBERTScore: Evaluating code generation with pretrained models of code,

Reference 40

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Observation dea723ac-3a95-4475-b761-f303707598b9 · outbound

This paper cites Accurate and efficient refactoring detection in commit history,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Accurate and efficient refactoring detection in commit history,

Reference 41

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Observation 2f73f4d1-e944-43d8-bf4e-9b9d5cb26549 · outbound

This paper cites Binary codes capable of correcting deletions, inser- tions, and reversals,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Binary codes capable of correcting deletions, inser- tions, and reversals,

Reference 42

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Observation 20d17fc7-5497-4e43-8722-e787940ddfcb · outbound

This paper cites Towards Understanding the Characteristics of Code Generation Errors Made by Large Language Models ,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Towards Understanding the Characteristics of Code Generation Errors Made by Large Language Models ,

Reference 43

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Observation d268fd13-a913-4d00-a871-0b0779774ca5 · outbound

This paper cites Can LLMs reason about program semantics? a comprehensive evaluation of LLMs on formal specification inference,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Can LLMs reason about program semantics? a comprehensive evaluation of LLMs on formal specification inference,

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-01T20:38:59.810606Z

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Observation e8fffa96-c86a-4c52-841d-d476a21e2ed5 · outbound

This paper cites Hints help finding and fixing bugs differently in python and text-based program representations,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Hints help finding and fixing bugs differently in python and text-based program representations,

Reference 45

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Observation 71af787a-57c8-4fda-9780-c0192cf59d78 · outbound

This paper cites Imitation game: Reproduc- ing deep learning bugs leveraging an intelligent agent,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Imitation game: Reproduc- ing deep learning bugs leveraging an intelligent agent,

Reference 46

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Observation ed282c0c-46a4-49e6-829e-3fc3a55b90b2 · outbound

This paper cites Zs4c: Zero-shot synthesis of compilable code for incomplete code snippets using llms,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Zs4c: Zero-shot synthesis of compilable code for incomplete code snippets using llms,

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-01T20:39:00.062426Z

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Observation da7d772a-3ad7-495e-b088-5ec36d6cf7a5 · outbound

This paper cites Selfpico: Self-guided partial code execution with llms,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Selfpico: Self-guided partial code execution with llms,

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-01T20:39:00.147778Z

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Observation 2d7357ea-b16e-49ce-a389-3dd0faabd2ab · outbound

This paper cites Evaluating large language models in class-level code generation,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Evaluating large language models in class-level code generation,

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-01T20:39:00.224401Z

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Observation 58af5cb1-ab5c-4ef7-9ee5-1d0c49ad4d4d · outbound

This paper cites Identifying patch correctness in test-based program repair,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Identifying patch correctness in test-based program repair,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-01T20:39:00.264514Z

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Observation e4cd068e-285a-4be0-86fe-50010ab3d885 · outbound

This paper cites ReprodGen Repository,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code ReprodGen Repository,

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-01T20:39:00.323748Z

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Observation 9d903af3-b7b7-4f6b-919e-ab691bfc547f · outbound

This paper cites ReprodGen Bench,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code ReprodGen Bench,

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-01T20:39:00.453850Z

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Observation f1533079-2d44-45bf-bf9b-3f7471ff3ac0 · outbound

This paper cites ReprodGen Leaderboard,.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code ReprodGen Leaderboard,

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-01T20:39:00.617619Z

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Observation 207b0dfe-636b-4a3e-bf36-a0dfea06520f · outbound

This paper cites Available: https://doi.org/10.1145/3728963.

From Discussion to Execution: Replicating Buggy and Correct Data Science Code Available: https://doi.org/10.1145/3728963

Reference 2025

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