Survey of GenAI for Automotive Software Development: From Requirements to Executable Code | Paper Citation Record

Observation eb630cce-ee26-45e8-8d91-97f2d8f3d8e8 · outbound

This paper cites an unresolved cited work.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Unresolved cited work

Reference 1

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Observation ead4b540-cc60-4e5f-8a9f-4f057e85781d · outbound

This paper cites Exploring generative ai in automated software engineering,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Exploring generative ai in automated software engineering,

Reference 2

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Observation d211b553-b1e0-4e2f-b01f-6eeda93a1277 · outbound

This paper cites Generative ai adoption in automotive vehicle technology: Case study of custom gpt,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Generative ai adoption in automotive vehicle technology: Case study of custom gpt,

Reference 3

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Observation ee8df169-b79a-4604-9346-ce4a48feb923 · outbound

This paper cites Auto- motive software engineering in an increasingly data-driven automotive sector,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Auto- motive software engineering in an increasingly data-driven automotive sector,

Reference 4

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Observation a5b731e8-55e5-4039-94e6-8dea9125e2f7 · outbound

This paper cites Requirements and software engineering for automotive perception systems: an interview study,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Requirements and software engineering for automotive perception systems: an interview study,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:49:03.556610Z

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Observation 57e60dea-d309-4bcd-9d22-38c440d0b01d · outbound

This paper cites Requirements management in automotive: Tools and trends for a competitive edge,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Requirements management in automotive: Tools and trends for a competitive edge,

Reference 6

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Observation 42ff040c-edf7-44e7-b956-dd77031accbd · outbound

This paper cites A Survey on Large Language Models with some Insights on their Capabilities and Limitations.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code A Survey on Large Language Models with some Insights on their Capabilities and Limitations

Reference 7

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Observation aa3ed4c5-26ea-4b82-a069-b7cf77b94227 · outbound

This paper cites Language Models are Few-Shot Learners.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Language Models are Few-Shot Learners

Reference 8

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Observation 3fa56de5-4cd8-45a2-8fab-2005b3845b6e · outbound

This paper cites Pre-train prompt fine-tune: A survey of prompting methods in natu- ral language processing,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Pre-train prompt fine-tune: A survey of prompting methods in natu- ral language processing,

Reference 9

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Observation 8d4db038-a5d3-449f-ad5f-7c81d1360589 · outbound

This paper cites Using the Retrieval- Augmented Generation to Improve the Question-Answering System in Human Health Risk Assessment: The Development and Application,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Using the Retrieval- Augmented Generation to Improve the Question-Answering System in Human Health Risk Assessment: The Development and Application,

Reference 10

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Observation c3911bec-1df8-44cc-b2c6-3ee826f0a6e6 · outbound

This paper cites Automating regulatory compliance: A multi-agent solution using Amazon Bedrock and CrewAI | Artificial Intelligence and Machine Learning,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Automating regulatory compliance: A multi-agent solution using Amazon Bedrock and CrewAI | Artificial Intelligence and Machine Learning,

Reference 11

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Observation 2e28d110-24e2-4637-896e-dd948d3edc44 · outbound

This paper cites Vision language models in autonomous driving: A survey and outlook,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Vision language models in autonomous driving: A survey and outlook,

Reference 12

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Observation 6f0ae2bc-65cf-4420-aba1-2c3dab16ef78 · outbound

This paper cites NotebookLM: Ai-powered research and note-taking assistant,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code NotebookLM: Ai-powered research and note-taking assistant,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:49:02.065549Z

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Observation 0ffb722f-d38e-4818-b7d0-1eb37aad92a8 · outbound

This paper cites MANNHEIM-CeCaS – Central Car Server – Supercomputing for Automotive,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code MANNHEIM-CeCaS – Central Car Server – Supercomputing for Automotive,

Reference 14

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Observation a847b231-f73d-479d-b0f0-0d1a4b0543aa · outbound

This paper cites Towards single-system illusion in software-defined vehicles - automated, ai-powered workflow,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Towards single-system illusion in software-defined vehicles - automated, ai-powered workflow,

Reference 15

Resolution: verified exact; raw_fallback, observed 2026-08-06T15:48:54.535496Z

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Observation 143b03ac-8b7e-444c-8a0b-9d840ac0c05b · outbound

This paper cites Synergy of large language model and model driven engineering for automated development of centralized vehicular systems,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Synergy of large language model and model driven engineering for automated development of centralized vehicular systems,

Reference 16

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Observation 5627c5e8-016b-4dd5-81cc-1cc9f45a5e57 · outbound

This paper cites Generative artificial intelligence for model-based graphical programming in automotive function development,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Generative artificial intelligence for model-based graphical programming in automotive function development,

Reference 17

Resolution: verified exact; doi, observed 2026-08-06T15:48:51.172230Z

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Observation 8f6446d2-3bde-4fa8-bd35-df9d8a2aa170 · outbound

This paper cites RECSIP: REpeated Clustering of Scores Improving the Precision.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code RECSIP: REpeated Clustering of Scores Improving the Precision

Reference 18

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Observation 0f581378-7db1-40a8-a2f7-1d41c48df6de · outbound

This paper cites Adopting rag for llm-aided future vehicle design,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Adopting rag for llm-aided future vehicle design,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:49:01.562286Z

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Observation 4a99cf90-e7a1-4f46-ba96-fb859d265b64 · outbound

This paper cites Local large language models to simplify requirement engineering documents in the automotive industry,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Local large language models to simplify requirement engineering documents in the automotive industry,

Reference 20

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Observation 980c0941-1199-4641-9980-2f5c023f9b13 · outbound

This paper cites Querying Large Automotive Software Models: Agentic vs. Direct LLM Approaches.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Querying Large Automotive Software Models: Agentic vs. Direct LLM Approaches

Reference 21

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Observation 6785ce23-a96d-43dc-a3e4-1cf989473046 · outbound

This paper cites Llm-based iterative approach to metamodeling in automotive,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Llm-based iterative approach to metamodeling in automotive,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:49:01.310805Z

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Observation 34da2b63-992a-4f10-a4b8-f81f2a5edd8a · outbound

This paper cites Llm-enabled instance model generation,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Llm-enabled instance model generation,

Reference 23

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Observation b1db050f-2aa5-4dbe-907a-36d5778029af · outbound

This paper cites Generative ai for ocl constraint generation: Dataset collection and llm fine-tuning,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Generative ai for ocl constraint generation: Dataset collection and llm fine-tuning,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:49:00.719833Z

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Observation 4dd95e5d-30a8-4fa0-b76e-004cdc17f731 · outbound

This paper cites Optimizing Retrieval Augmented Generation for Object Constraint Language.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Optimizing Retrieval Augmented Generation for Object Constraint Language

Reference 25

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Observation 08559745-6a99-46c6-a9e9-ad850e596a2d · outbound

This paper cites Specbook-copilot – efficient formalization of requirements using artificial intelligence in the development of mb.os,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Specbook-copilot – efficient formalization of requirements using artificial intelligence in the development of mb.os,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:49:00.428677Z

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Observation 31d59a06-c44d-434c-b28c-0715f838cbf0 · outbound

This paper cites Chat2Scenario: Scenario Extraction From Dataset Through Utilization of Large Language Model.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Chat2Scenario: Scenario Extraction From Dataset Through Utilization of Large Language Model

Reference 27

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This paper cites TARGET: Automated Scenario Generation from Traffic Rules for Testing Autonomous Vehicles via Validated LLM-Guided Knowledge Extraction.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code TARGET: Automated Scenario Generation from Traffic Rules for Testing Autonomous Vehicles via Validated LLM-Guided Knowledge Extraction

Reference 28

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Observation 3d14f56f-5017-4033-8029-42b08da41fea · outbound

This paper cites LMM-enhanced Safety-Critical Scenario Generation for Autonomous Driving System Testing From Non-Accident Traffic Videos.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code LMM-enhanced Safety-Critical Scenario Generation for Autonomous Driving System Testing From Non-Accident Traffic Videos

Reference 29

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Observation f354c829-514e-408e-bdd5-2029511a0c28 · outbound

This paper cites LeGEND: A Top-Down Approach to Scenario Generation of Autonomous Driving Systems Assisted by Large Language Models.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code LeGEND: A Top-Down Approach to Scenario Generation of Autonomous Driving Systems Assisted by Large Language Models

Reference 30

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Observation ede10756-5d1f-4918-a4d3-bd347758c1d6 · outbound

This paper cites Towards specification-driven llm- based generation of embedded automotive software,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Towards specification-driven llm- based generation of embedded automotive software,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:49:00.113847Z

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Observation 17aa2763-0c3b-47f9-997b-cd9bd1b2303f · outbound

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

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code An empirical study of the code generation of safety-critical software using llms,

Reference 32

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Observation 70e7cb5d-03b5-4707-ab5a-a3a125878511 · outbound

This paper cites On simulation-guided llm-based code generation for safe autonomous driving software,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code On simulation-guided llm-based code generation for safe autonomous driving software,

Reference 33

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Observation edf77b9c-1a45-4b53-a8d9-b2e3ea170b26 · outbound

This paper cites Automating automotive software development: A synergy of generative ai and formal methods,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Automating automotive software development: A synergy of generative ai and formal methods,

Reference 34

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Observation 4a0d39f5-381c-40e9-8e33-6755dad5173f · outbound

This paper cites Are requirements really all you need? A case study of LLM-driven configuration code generation for automotive simulations.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Are requirements really all you need? A case study of LLM-driven configuration code generation for automotive simulations

Reference 35

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Observation 1a57a632-7919-4bac-9ee2-48ef27d7b520 · outbound

This paper cites Llm-driven testing for autonomous driving scenarios,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Llm-driven testing for autonomous driving scenarios,

Reference 36

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Observation 073a27a9-2611-45fc-8d8e-c9dc235456b3 · outbound

This paper cites Survey of hallucination in natural language generation,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Survey of hallucination in natural language generation,

Reference 37

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Observation 2e305797-b359-4e6b-910c-df3d006a6607 · outbound

This paper cites Encouraging divergent thinking in large language models through multi-agent debate,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Encouraging divergent thinking in large language models through multi-agent debate,

Reference 38

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Observation 52d9da28-9288-418b-8506-fa0d4d751347 · outbound

This paper cites ReConcile: Round-table conference improves reasoning via consensus among diverse LLMs,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code ReConcile: Round-table conference improves reasoning via consensus among diverse LLMs,

Reference 39

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Observation 2c067439-6ae3-462b-bd48-ca69149a165c · outbound

This paper cites Rethinking the bounds of LLM reasoning: Are multi-agent discussions the key?.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Rethinking the bounds of LLM reasoning: Are multi-agent discussions the key?

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:48:58.225888Z

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Observation 7eff8fbb-a6d0-4a61-89e2-23c7ef7f7e3f · outbound

This paper cites Self-consistency improves chain of thought reasoning in language models,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Self-consistency improves chain of thought reasoning in language models,

Reference 41

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Observation d50a4cca-baf9-4a50-bcd9-b8c6b3b7be65 · outbound

This paper cites Reasoning Aware Self-Consistency: Leveraging Reasoning Paths for Efficient LLM Sampling.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Reasoning Aware Self-Consistency: Leveraging Reasoning Paths for Efficient LLM Sampling

Reference 42

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Observation 78f71e1c-b0f9-426a-a780-521e6aff7ca9 · outbound

This paper cites Internal Consistency and Self-Feedback in Large Language Models: A Survey.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Internal Consistency and Self-Feedback in Large Language Models: A Survey

Reference 43

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This paper cites Escape sky-high cost: Early-stopping self-consistency for multi-step reasoning,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Escape sky-high cost: Early-stopping self-consistency for multi-step reasoning,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:48:57.975538Z

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Observation 48ebbae2-af95-47a1-b352-83cd3cb00326 · outbound

This paper cites Let’s sample step by step: Adaptive-consistency for efficient reasoning and coding with LLMs,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Let’s sample step by step: Adaptive-consistency for efficient reasoning and coding with LLMs,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:48:57.745501Z

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Observation 2a8734b4-9ee0-440e-bd35-96935f357225 · outbound

This paper cites SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:48:57.432640Z

Source-reported events for the cited work

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Observation 3919c584-7db8-4760-a804-1cc89128cc57 · outbound

This paper cites Evaluating uncertainty-based failure detection for closed-loop LLM planners,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Evaluating uncertainty-based failure detection for closed-loop LLM planners,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:48:57.170888Z

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Observation 77321195-86b6-482b-be87-f12e1d8d4271 · outbound

This paper cites CLUE: Concept-Level Uncertainty Estimation for Large Language Models.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code CLUE: Concept-Level Uncertainty Estimation for Large Language Models

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T15:48:47.673570Z

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Observation d025c167-3784-401d-82f5-bb40e17e4434 · outbound

This paper cites Geneva, Switzerland: ISO, 2018.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Geneva, Switzerland: ISO, 2018

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:48:56.918729Z

Source-reported events for the cited work

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Observation 77614bac-fa67-447b-88af-786e5542873f · outbound

This paper cites Cppcheck: A static analysis tool for c++,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Cppcheck: A static analysis tool for c++,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:48:56.672425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c1663745-2309-4efd-b846-785f62a68238 · outbound

This paper cites Multimodal chain-of-thought reasoning in language models,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Multimodal chain-of-thought reasoning in language models,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:48:56.421469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 818418cd-f533-4854-b5b0-ccc61331aeef · outbound

This paper cites Better zero-shot reasoning with role-play prompting,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Better zero-shot reasoning with role-play prompting,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:48:56.239945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bb0b8114-f0dc-44de-b69b-5e560105e54e · outbound

This paper cites Langprop: A code optimization framework using large language models applied to driving,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Langprop: A code optimization framework using large language models applied to driving,

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T15:48:48.413355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18b76216-824e-41f9-84e2-8b245f09a15a · outbound

This paper cites Vecogen: Automating generation of formally verified c code with large language models,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Vecogen: Automating generation of formally verified c code with large language models,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:48:56.042054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 12b33664-c80e-4c68-8197-598a871ae5cd · outbound

This paper cites Better Zero-Shot Reasoning with Role-Play Prompting.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Better Zero-Shot Reasoning with Role-Play Prompting

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T15:48:48.298639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 152494c3-7c23-43a2-bc11-935be59db08a · outbound

This paper cites Generating Automotive Code: Large Language Models for Software Development and Verification in Safety-Critical Systems.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Generating Automotive Code: Large Language Models for Software Development and Verification in Safety-Critical Systems

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:48:53.276261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 23a294b8-da9f-44c4-872a-abecfc3bdf58 · outbound

This paper cites A VL CAMEO 5™,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code A VL CAMEO 5™,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:48:55.783200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1a2882d5-d713-4da9-aadc-31350169309d · outbound

This paper cites (2025) Sysml.org.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code (2025) Sysml.org

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:48:55.510090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ac0cbf16-ce7c-4999-b567-948bf28c669a · outbound

This paper cites Speedgen: Enhancing code ef- ficiency through large language model-based performance optimization,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Speedgen: Enhancing code ef- ficiency through large language model-based performance optimization,

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T15:48:48.806654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0e39c43-e819-45fa-8ed3-1979f86a3393 · outbound

This paper cites Evaluation of vision language model on uml diagrams,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Evaluation of vision language model on uml diagrams,

Reference 60

Resolution: verified exact; raw_fallback, observed 2026-08-06T15:48:53.012836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0320b4fa-3aa1-4bd2-b947-5371c8880304 · outbound

This paper cites Beyond End-to-End VLMs: Leveraging Intermediate Text Representations for Superior Flowchart Understanding.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Beyond End-to-End VLMs: Leveraging Intermediate Text Representations for Superior Flowchart Understanding

Reference 61

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:48:52.671511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2a6b2b9b-0141-441b-8480-f1a035e11916 · outbound

This paper cites Arrow-Guided VLM: Enhancing Flowchart Understanding via Arrow Direction Encoding.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Arrow-Guided VLM: Enhancing Flowchart Understanding via Arrow Direction Encoding

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-06T15:48:49.654506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08a8534d-681f-43ae-ac9b-aeaabc74e318 · outbound

This paper cites Multi-modal Summarization in Model-Based Engineering: Automotive Software Development Case Study.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Multi-modal Summarization in Model-Based Engineering: Automotive Software Development Case Study

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T15:48:49.277484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9ca77e8-f3c8-43a9-8bf7-663f91d23e29 · outbound

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

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-06T15:48:49.861456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 640dac34-1a76-4843-b400-dd9c9cd5f2d7 · outbound

This paper cites Towards Specification-Driven LLM-Based Generation of Embedded Automotive Software.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Towards Specification-Driven LLM-Based Generation of Embedded Automotive Software

Reference 65

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:48:52.344781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f3e17ca1-d703-4608-b508-7b89c9a857e4 · outbound

This paper cites Aegis:An Advanced LLM-Based Multi-Agent for Intelligent Functional Safety Engineering.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Aegis:An Advanced LLM-Based Multi-Agent for Intelligent Functional Safety Engineering

Reference 66

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:48:52.111788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6b141e87-ef25-4a75-838a-3ee9059590f3 · outbound

This paper cites Chain-of-region: Visual language models need details for diagram analysis,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Chain-of-region: Visual language models need details for diagram analysis,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:48:55.288765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b1e6d8e6-35db-4425-8324-ae78627ab074 · outbound

This paper cites Harnessing the power of large language models for automated code generation and verification,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Harnessing the power of large language models for automated code generation and verification,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:48:55.064391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ee4c49f9-6f58-4b7c-a752-418127b8be00 · outbound

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

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code CodeBERT: A Pre-Trained Model for Programming and Natural Languages

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-06T15:48:50.521586Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb24d86b-7cb2-44dd-8760-6b4969eb73d7 · outbound

This paper cites Startup anthropic says its new ai model can code for hours at a time,.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Startup anthropic says its new ai model can code for hours at a time,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:48:54.807451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 22b1c87a-6132-4b5c-a81f-488151c4097b · outbound

This paper cites On Simulation-Guided LLM-based Code Generation for Safe Autonomous Driving Software.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code On Simulation-Guided LLM-based Code Generation for Safe Autonomous Driving Software

Reference 71

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:48:51.834426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b6446e8b-6710-48ae-9720-d70b5f24c0b3 · outbound

This paper cites Large Language Models are Zero-Shot Reasoners.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Large Language Models are Zero-Shot Reasoners

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-06T15:48:50.931991Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 514cd4b0-b4ad-41f0-999a-bb7a3d59be2a · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-06T15:48:50.823083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1ba0ca2-abee-40c5-bf70-eeacd4a497de · outbound

This paper cites Available: http://dx.doi.org/10.1145/3571730.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Available: http://dx.doi.org/10.1145/3571730

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T15:48:46.290973Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5cf84750-aead-4626-9ada-7b0ecc936139 · outbound

This paper cites Available: https://doi.org/10.1007/s00766-023-00410-1.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code Available: https://doi.org/10.1007/s00766-023-00410-1

Reference 2024

Resolution: verified exact; doi, observed 2026-08-06T15:48:51.381546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 22fb5feb-caf1-49c1-967f-905deeb51dd4 · outbound

This paper cites VeCoGen: Automating Generation of Formally Verified C Code with Large Language Models.

Survey of GenAI for Automotive Software Development: From Requirements to Executable Code VeCoGen: Automating Generation of Formally Verified C Code with Large Language Models

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T15:48:48.641759Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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