Paper Citation Record · LEDGER

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation

As of 13 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 2 inbound Pith citation observations for arXiv:2404.01535.

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

pith.paper-citation-record.v1
2404.01535 v2

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-24T02:19:23.135463Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:35:26.462971Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-11T17:26:04.804438Z

Reference resolution

47 of 47 outbound references displayed

verified exact15
verified fuzzy30
unresolved2
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8c6233f9-62c9-4ab1-9e52-2edf537f0b41 · outbound

This paper cites AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-05-24T02:23:46.140208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d1783f87-b459-4b56-b443-538823ccd140 · outbound

This paper cites An empirical study of the code generation of safety-critical software using llms.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation An empirical study of the code generation of safety-critical software using llms

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T02:58:48.429631Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8692cacc-1bc5-487c-98e2-1df0a9dfe96c · outbound

This paper cites Exploring early adopters’ perceptions of chatgpt as a code generation tool.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Exploring early adopters’ perceptions of chatgpt as a code generation tool

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T02:58:48.450089Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a707159b-4cc7-41d6-8d25-1b95998c4b70 · outbound

This paper cites Ai2: Safety and robustness certification of neural networks with abstract interpretation.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Ai2: Safety and robustness certification of neural networks with abstract interpretation

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T02:58:48.447026Z

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Observation 7722fda5-815f-4ef7-b811-2d78240e8f20 · outbound

This paper cites Chatgpt for programming numerical methods.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Chatgpt for programming numerical methods

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T02:58:48.432558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d3a4a5af-fdf4-48af-801f-d4e862b8dae6 · outbound

This paper cites Large Language Models for Software Engineering: Survey and Open Problems.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Large Language Models for Software Engineering: Survey and Open Problems

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-24T02:23:46.164298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8a7b00a2-b9be-43c6-8465-be36e653abe3 · outbound

This paper cites an unresolved cited work.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-05-24T02:58:48.437468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 61e71a6b-bfbe-457b-aa8a-681fe11a86cb · outbound

This paper cites an unresolved cited work.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-05-24T02:58:48.455266Z

Source-reported events for the cited work

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Observation af9f927d-f35f-4b1f-b620-8f524c2121fb · outbound

This paper cites Gpt-4 technical report.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Gpt-4 technical report

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T02:58:48.443796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e8b9e1a8-b2e7-43ed-85cb-27d49b46c16f · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Evaluating Large Language Models Trained on Code

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-05-24T02:23:46.128748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a96cf442-b48b-4a27-a6c0-a95915d60308 · outbound

This paper cites Competition- level code generation with alphacode.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Competition- level code generation with alphacode

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T02:58:48.440726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 37a569f8-87cc-4f6e-93e0-9c9d5b54aa8e · outbound

This paper cites CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-05-24T02:23:46.169645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 983f0295-553d-49d1-82fe-f06fe1da0b4b · outbound

This paper cites Improving chatgpt prompt for code generation.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Improving chatgpt prompt for code generation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T02:58:48.435021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0727736f-1971-451c-9f0b-07af7e1f2670 · outbound

This paper cites Llm is like a box of chocolates: the non-determinism of chatgpt in code generation.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Llm is like a box of chocolates: the non-determinism of chatgpt in code generation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T02:58:48.465218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation bd4ab707-2c90-4b58-b023-5b7e5c6aa594 · outbound

This paper cites A comparative study of code generation using chatgpt 3.5 across 10 programming languages.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation A comparative study of code generation using chatgpt 3.5 across 10 programming languages

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T02:58:48.473339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b3719464-84b2-4e64-a4ce-3b84d0beb58b · outbound

This paper cites A systematic evaluation of large language models of code.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation A systematic evaluation of large language models of code

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T02:58:48.470363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5d1b62fa-3040-4a67-882b-b3de135326bc · outbound

This paper cites Discovering the syntax and strategies of natural language programming with generative language models.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Discovering the syntax and strategies of natural language programming with generative language models

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T02:58:48.462373Z

Source-reported events for the cited work

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Observation 8c112c3b-eabc-4df9-8d64-5e0e89b286c3 · outbound

This paper cites Automatic Semantic Augmentation of Language Model Prompts (for Code Summarization).

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Automatic Semantic Augmentation of Language Model Prompts (for Code Summarization)

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-24T02:23:46.185956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 52e5b055-642d-457c-bcf6-c1df0bde7756 · outbound

This paper cites Evaluating the code quality of ai-assisted code generation tools: An empirical study on github copilot, amazon codewhisperer, and chatgpt.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Evaluating the code quality of ai-assisted code generation tools: An empirical study on github copilot, amazon codewhisperer, and chatgpt

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T02:58:48.459113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 45d9e0e4-4130-4d53-ac9b-69d403d0189a · outbound

This paper cites Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T02:58:48.530453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e43c588f-52be-4ee6-93e1-e84185ca45bb · outbound

This paper cites AceCoder: Utilizing Existing Code to Enhance Code Generation.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation AceCoder: Utilizing Existing Code to Enhance Code Generation

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-24T02:23:46.151950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation bb54cf4e-ee19-48a6-9c4f-d4f154fa64c3 · outbound

This paper cites Piloting Copilot, Codex, and StarCoder2: Hot Temperature, Cold Prompts, or Black Magic?.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Piloting Copilot, Codex, and StarCoder2: Hot Temperature, Cold Prompts, or Black Magic?

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-24T02:23:46.157957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f55fcf35-99be-4496-b89c-14bbcf9deae0 · outbound

This paper cites Controlling large language models to generate secure and vulnerable code.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Controlling large language models to generate secure and vulnerable code

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T02:58:48.527463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3912cf41-b336-49eb-8bb4-d812bbc2fff1 · outbound

This paper cites ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-24T02:23:46.192371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2255bea7-8e34-44b7-9189-b0d37f27e127 · outbound

This paper cites Skcoder: A sketch- based approach for automatic code generation.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Skcoder: A sketch- based approach for automatic code generation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T02:58:48.524517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 12afd70d-5f50-4f06-bf81-1f6f0b6d11dc · outbound

This paper cites Structured Chain-of-Thought Prompting for Code Generation.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Structured Chain-of-Thought Prompting for Code Generation

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-24T02:23:46.110081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 74942462-14c6-459b-b23b-5be498f7b7ca · outbound

This paper cites SelfEvolve: A Code Evolution Framework via Large Language Models.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation SelfEvolve: A Code Evolution Framework via Large Language Models

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-24T02:23:46.116830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1b91a291-db47-43df-847d-67a29c3ef1f7 · outbound

This paper cites GPT-4 Technical Report.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation GPT-4 Technical Report

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-05-24T02:23:46.145956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a3f13fc8-8b45-49ed-bace-4b3494f1e763 · outbound

This paper cites Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-24T02:23:46.135108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a21a166f-d735-455f-9731-7ff1e62fcb51 · outbound

This paper cites A Categorical Archive of ChatGPT Failures.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation A Categorical Archive of ChatGPT Failures

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-24T02:23:46.122783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3039b614-7ec3-4144-b47d-44f5d976c998 · outbound

This paper cites Large language models of code fail at completing code with potential bugs.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Large language models of code fail at completing code with potential bugs

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T02:58:48.521756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4a01d87c-462a-49fe-aca8-02cc9280f75a · outbound

This paper cites COCO: Testing Code Generation Systems via Concretized Instructions.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation COCO: Testing Code Generation Systems via Concretized Instructions

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-24T02:23:46.174652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8c69fa5f-b43a-4fbe-a45d-45c35e69c58a · outbound

This paper cites On the robustness of code generation techniques: An empirical study on github copilot.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation On the robustness of code generation techniques: An empirical study on github copilot

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T02:58:48.514738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c203eaf6-6d34-4d5b-97a3-f600a48c0e8e · outbound

This paper cites The marabou framework for verification and analysis of deep neural networks.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation The marabou framework for verification and analysis of deep neural networks

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T02:58:48.517483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8edf17cf-614b-4b4b-9c23-80dce08215c5 · outbound

This paper cites Reluplex: An efficient smt solver for verifying deep neural networks.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Reluplex: An efficient smt solver for verifying deep neural networks

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T02:58:48.512072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 277d1a3e-7fc7-4f95-99c0-561767db9d53 · outbound

This paper cites Piecewise linear neural networks verification: A comparative study.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Piecewise linear neural networks verification: A comparative study

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T02:58:48.509288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b932267a-048a-4377-9bcb-dd2326650995 · outbound

This paper cites Branch and bound for piecewise linear neural network verification.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Branch and bound for piecewise linear neural network verification

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T02:58:48.506750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d66cd76a-a64a-431b-a8fa-61fde29c17bd · outbound

This paper cites Concolic testing for deep neural networks.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Concolic testing for deep neural networks

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T02:58:48.503835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9f7a29e5-801e-4c37-b760-cf875bdce038 · outbound

This paper cites Robustness verification of classification deep neural networks via linear programming.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Robustness verification of classification deep neural networks via linear programming

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T02:58:48.501134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d023bc46-0275-4191-a0b2-be6cd23850d1 · outbound

This paper cites Fast and effective robustness certification.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Fast and effective robustness certification

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T02:58:48.498373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 546fa416-c1f2-4361-91c4-320fc8bcf55c · outbound

This paper cites An abstract domain for certifying neural networks.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation An abstract domain for certifying neural networks

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T02:58:48.489056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation dcb50ce1-e799-4f41-b6a4-3fc31067eea8 · outbound

This paper cites Formal security analysis of neural networks using symbolic intervals.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Formal security analysis of neural networks using symbolic intervals

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T02:58:48.495416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cd9b02ad-4f81-4e27-89c2-5aef27134e45 · outbound

This paper cites Scalable quantitative verification for deep neural networks.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Scalable quantitative verification for deep neural networks

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T02:58:48.485363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation db813d53-cfd0-4c90-b593-eba167c9309f · outbound

This paper cites Deephunter: a coverage-guided fuzz testing framework for deep neural networks.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Deephunter: a coverage-guided fuzz testing framework for deep neural networks

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T02:58:48.482711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation bf1c9bad-605c-4d1c-8a6e-1003214e2ca4 · outbound

This paper cites Metamorphic Testing: A New Approach for Generating Next Test Cases.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Metamorphic Testing: A New Approach for Generating Next Test Cases

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-05-24T02:23:46.180357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 466737bb-74cb-49bd-b969-5bbc611b1b7b · outbound

This paper cites Large language models: The next frontier for variable discovery within metamorphic testing?.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Large language models: The next frontier for variable discovery within metamorphic testing?

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T02:58:48.479165Z

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Observation d0d53004-e57e-4a4a-a5f0-5573f7166fac · outbound

This paper cites Assessing robustness of ml-based program analysis tools using metamorphic program transforma- tions.

Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation Assessing robustness of ml-based program analysis tools using metamorphic program transforma- tions

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T02:58:48.476132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

Observation 61406e0d-cd5e-4474-bc89-0a68d815bc69 · inbound

Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains cites this paper.

Can LLMs faithfully generate their layperson-understandable 'self'?: A Case Study in High-Stakes Domains Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T13:35:26.462971Z

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Observation 207ffcd3-351d-4add-811e-248aca4a6eb2 · 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 Assessing, Exploiting, and Mitigating Syntactic Robustness Failures in LLM-Based Code Generation

Reference 109

Resolution: verified exact; local_arxiv, observed 2026-05-11T17:26:04.807919Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.