Paper Citation Record · LEDGER

Can LLMs Generate User Stories and Assess Their Quality?

As of 19 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 1 inbound Pith citation observation for arXiv:2507.15157.

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

pith.paper-citation-record.v1
2507.15157 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:46:35.724935Z

measured 56 of 56 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T11:09:38.774255Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-04T12:16:13.904932Z

Reference resolution

55 of 55 outbound references displayed

verified exact3
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a3da33bf-9e2a-4bb5-93e8-ea9f9e5c9544 · outbound

This paper cites Detecting Terminological Ambiguity in User Stories: Tool and Experimentation,.

Can LLMs Generate User Stories and Assess Their Quality? Detecting Terminological Ambiguity in User Stories: Tool and Experimentation,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.728831Z

Source-reported events for the cited work

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Observation d47f0421-487c-45d1-b0f4-340009909627 · outbound

This paper cites Improving Agile Requirements: The Quality User Story Framework and Tool,.

Can LLMs Generate User Stories and Assess Their Quality? Improving Agile Requirements: The Quality User Story Framework and Tool,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.714980Z

Source-reported events for the cited work

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Observation 121bd899-bf57-42f2-8154-7d5555e98e14 · outbound

This paper cites Systematic Literature Mapping of User Story Research,.

Can LLMs Generate User Stories and Assess Their Quality? Systematic Literature Mapping of User Story Research,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.701681Z

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Observation f8c01246-f275-453b-b6db-dd82112f2dbd · outbound

This paper cites A Systematic Literature Review on Agile Requirements Engineering Practices and Challenges,.

Can LLMs Generate User Stories and Assess Their Quality? A Systematic Literature Review on Agile Requirements Engineering Practices and Challenges,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.687928Z

Source-reported events for the cited work

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Observation b4083ccd-08c6-4c7f-b4e3-8bfe61cec275 · outbound

This paper cites Requirements Engineering Challenges and Practices in Large- Scale Agile System Development,.

Can LLMs Generate User Stories and Assess Their Quality? Requirements Engineering Challenges and Practices in Large- Scale Agile System Development,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.674273Z

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Observation bff57e33-2399-497c-98b2-3dfb18c2a148 · outbound

This paper cites Forging High-Quality User Stories: Towards a Discipline for Agile Re- quirements,.

Can LLMs Generate User Stories and Assess Their Quality? Forging High-Quality User Stories: Towards a Discipline for Agile Re- quirements,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.660528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8a026208-9876-4efc-9135-543350341d75 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Can LLMs Generate User Stories and Assess Their Quality? BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 7

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Source-reported events for the cited work

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Observation 985a1f8b-5bd7-4334-99d5-69cfba53368d · outbound

This paper cites Language Models Are Unsupervised Multitask Learners,.

Can LLMs Generate User Stories and Assess Their Quality? Language Models Are Unsupervised Multitask Learners,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.643228Z

Source-reported events for the cited work

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Observation ea42ef8d-8da0-4c2b-89f1-39a34a9459f6 · outbound

This paper cites Language Models Are Few-Shot Learners,.

Can LLMs Generate User Stories and Assess Their Quality? Language Models Are Few-Shot Learners,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.629387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 03c66ad2-f2ea-493b-b089-4e41a954163b · outbound

This paper cites Large Language Models for Software Engineer- ing: Survey and Open Problems ,.

Can LLMs Generate User Stories and Assess Their Quality? Large Language Models for Software Engineer- ing: Survey and Open Problems ,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.616134Z

Source-reported events for the cited work

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Observation 34286749-256e-43e3-a56b-6ac45cab7243 · outbound

This paper cites Evaluating Large Language Models in Class-Level Code Generation,.

Can LLMs Generate User Stories and Assess Their Quality? Evaluating Large Language Models in Class-Level Code Generation,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.602766Z

Source-reported events for the cited work

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Observation 1f9714e3-0296-486c-b346-01af923d3944 · outbound

This paper cites Studying LLM Performance on Closed- and Open-source Data.

Can LLMs Generate User Stories and Assess Their Quality? Studying LLM Performance on Closed- and Open-source Data

Reference 12

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Observation 0e403372-48ef-4ac5-b336-d2161350008e · outbound

This paper cites Inferfix: End-to-end Program Repair with LLMs,.

Can LLMs Generate User Stories and Assess Their Quality? Inferfix: End-to-end Program Repair with LLMs,

Reference 13

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Source-reported events for the cited work

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Observation c1c39aaa-f97c-43f2-b74d-23090cc7cf98 · outbound

This paper cites Automated Program Repair in the Era of Large Pre-Trained Language Models,.

Can LLMs Generate User Stories and Assess Their Quality? Automated Program Repair in the Era of Large Pre-Trained Language Models,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.576000Z

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Observation 0c115f25-b4b6-47a6-a404-b4b9ae189260 · outbound

This paper cites Copiloting the Copilots: Fusing Large Language Models with Completion Engines for Automated Program Repair,.

Can LLMs Generate User Stories and Assess Their Quality? Copiloting the Copilots: Fusing Large Language Models with Completion Engines for Automated Program Repair,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.562566Z

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Observation 93f91442-193c-4e79-b823-431316b6d888 · outbound

This paper cites Using an LLM to Help With Code Understanding,.

Can LLMs Generate User Stories and Assess Their Quality? Using an LLM to Help With Code Understanding,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.549447Z

Source-reported events for the cited work

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Observation 7191172b-902e-4d59-9e44-6188c9167911 · outbound

This paper cites Advancing Requirements Engineering through Generative AI: Assessing the Role of LLMs,.

Can LLMs Generate User Stories and Assess Their Quality? Advancing Requirements Engineering through Generative AI: Assessing the Role of LLMs,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.535725Z

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Observation e8f9eba3-e32c-42ec-8966-2ef9cd8ed515 · outbound

This paper cites How do requirements evolve during elicitation? an empirical study combining interviews and app store analysis,.

Can LLMs Generate User Stories and Assess Their Quality? How do requirements evolve during elicitation? an empirical study combining interviews and app store analysis,

Reference 18

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Observation a2291fa7-acb4-40e3-bfa0-982afab896d3 · outbound

This paper cites Interrater reliability: the kappa statistic,.

Can LLMs Generate User Stories and Assess Their Quality? Interrater reliability: the kappa statistic,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.508789Z

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Observation 03ab1bfa-9e41-4130-8c48-471ffe05a6a8 · outbound

This paper cites The effect of sampling temperature on problem solving in large language models,.

Can LLMs Generate User Stories and Assess Their Quality? The effect of sampling temperature on problem solving in large language models,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.495607Z

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Observation 6dd7651d-73f1-4de0-b066-efc0f487273a · outbound

This paper cites Improving agile requirements: the quality user story framework and tool,.

Can LLMs Generate User Stories and Assess Their Quality? Improving agile requirements: the quality user story framework and tool,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.481089Z

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Observation fdb5e663-fa53-4055-8271-f2a185b71299 · outbound

This paper cites Empirical research methods in web and software engineering,.

Can LLMs Generate User Stories and Assess Their Quality? Empirical research methods in web and software engineering,

Reference 22

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Observation 2f9cea6b-07eb-4557-bf49-8b5612c43343 · outbound

This paper cites Quantifying Memorization Across Neural Language Models.

Can LLMs Generate User Stories and Assess Their Quality? Quantifying Memorization Across Neural Language Models

Reference 23

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Source-reported events for the cited work

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Observation 82a5e0c3-2770-4ade-b744-4125cf7312f4 · outbound

This paper cites Application of Large Language Models to Software Engi- neering Tasks: Opportunities, Risks, and Implications,.

Can LLMs Generate User Stories and Assess Their Quality? Application of Large Language Models to Software Engi- neering Tasks: Opportunities, Risks, and Implications,

Reference 24

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Observation 19ef4e83-418b-4f99-9e4d-d7d1c8f6e22c · outbound

This paper cites On the use of GPT-4 for creating goal models: An exploratory study,.

Can LLMs Generate User Stories and Assess Their Quality? On the use of GPT-4 for creating goal models: An exploratory study,

Reference 25

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Observation 2572ada2-6fc3-43b2-bd49-6cdd64bbdcb4 · outbound

This paper cites Automated domain modeling with large language models: A comparative study,.

Can LLMs Generate User Stories and Assess Their Quality? Automated domain modeling with large language models: A comparative study,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.425452Z

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Observation ffa84183-d61b-4e03-9dfe-9acd918fc3f7 · outbound

This paper cites Towards taming large language models with prompt templates for legal GRL modeling,.

Can LLMs Generate User Stories and Assess Their Quality? Towards taming large language models with prompt templates for legal GRL modeling,

Reference 27

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Observation c96aa0c1-06a8-4e24-8247-5d4b5a373bdd · outbound

This paper cites On the assessment of generative ai in modeling tasks: an experience report with chatgpt and uml,.

Can LLMs Generate User Stories and Assess Their Quality? On the assessment of generative ai in modeling tasks: an experience report with chatgpt and uml,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.398441Z

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Observation 1aa4b748-20b4-4328-a805-9f595fa732a1 · outbound

This paper cites Prompts matter: Insights and strategies for prompt engineering in automated software traceability,.

Can LLMs Generate User Stories and Assess Their Quality? Prompts matter: Insights and strategies for prompt engineering in automated software traceability,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.385073Z

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Observation 28e1ef39-0f4e-4abe-8303-80770663142b · outbound

This paper cites Code Gradients: Towards Automated Traceability of LLM-Generated Code,.

Can LLMs Generate User Stories and Assess Their Quality? Code Gradients: Towards Automated Traceability of LLM-Generated Code,

Reference 30

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Observation 49f489c2-85c1-4d74-9d21-67ca3772053e · outbound

This paper cites Requirements are All You Need: From Requirements to Code with LLMs.

Can LLMs Generate User Stories and Assess Their Quality? Requirements are All You Need: From Requirements to Code with LLMs

Reference 31

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This paper cites Research directions for using llm in software requirement engineering: a systematic review,.

Can LLMs Generate User Stories and Assess Their Quality? Research directions for using llm in software requirement engineering: a systematic review,

Reference 32

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Observation fbc44bc2-a4e6-4698-8976-28d16e4b05a3 · outbound

This paper cites Generative ai for requirements engineering: A systematic literature review,.

Can LLMs Generate User Stories and Assess Their Quality? Generative ai for requirements engineering: A systematic literature review,

Reference 33

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This paper cites Using chatgpt in software requirements engineering: A comprehensive review,.

Can LLMs Generate User Stories and Assess Their Quality? Using chatgpt in software requirements engineering: A comprehensive review,

Reference 34

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Observation a7b7160a-2b35-4246-9835-d5894c5c310e · outbound

This paper cites Improving requirements completeness: Automated assistance through large language models,.

Can LLMs Generate User Stories and Assess Their Quality? Improving requirements completeness: Automated assistance through large language models,

Reference 35

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Observation 9e7169d5-7dd6-4183-b06e-af0eac2997c7 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Can LLMs Generate User Stories and Assess Their Quality? BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 36

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This paper cites Inconsistency Detec- tion in Natural Language Requirements Using Chatgpt: A Preliminary Evaluation,.

Can LLMs Generate User Stories and Assess Their Quality? Inconsistency Detec- tion in Natural Language Requirements Using Chatgpt: A Preliminary Evaluation,

Reference 37

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

Can LLMs Generate User Stories and Assess Their Quality? Chatgpt Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.303729Z

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Observation f358071f-c108-4547-8827-aba32f3b7c6a · outbound

This paper cites Generating Requirements Elicitation Interview Scripts with Large Language Models,.

Can LLMs Generate User Stories and Assess Their Quality? Generating Requirements Elicitation Interview Scripts with Large Language Models,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.290439Z

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Observation 96c321a8-13ae-4845-bb70-970e23297193 · outbound

This paper cites Teaching Requirements Elicitation Interviews: An Empirical Study of Learning from Mistakes,.

Can LLMs Generate User Stories and Assess Their Quality? Teaching Requirements Elicitation Interviews: An Empirical Study of Learning from Mistakes,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.277250Z

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Observation 89a6f80e-9a13-4d6f-9248-71ecf62f39a7 · outbound

This paper cites Elicitron: An LLM Agent-Based Simulation Framework for Design Requirements Elicitation.

Can LLMs Generate User Stories and Assess Their Quality? Elicitron: An LLM Agent-Based Simulation Framework for Design Requirements Elicitation

Reference 42

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This paper cites Strategies, Benefits and Challenges of App Store- inspired Requirements Elicitation,.

Can LLMs Generate User Stories and Assess Their Quality? Strategies, Benefits and Challenges of App Store- inspired Requirements Elicitation,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.263443Z

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Observation d7a5aa2b-eaeb-4864-b5de-126871c7f9a9 · outbound

This paper cites Translating requirements in property specification patterns using llms,.

Can LLMs Generate User Stories and Assess Their Quality? Translating requirements in property specification patterns using llms,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.249725Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2648ce56-cdf8-468e-bad7-3ce400e2a68f · outbound

This paper cites nl2spec: Interactively translating unstructured natural language to temporal logics with large language models,.

Can LLMs Generate User Stories and Assess Their Quality? nl2spec: Interactively translating unstructured natural language to temporal logics with large language models,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.234864Z

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Observation 055edf09-3b4f-4424-81b2-6b6706695b17 · outbound

This paper cites Exploring LLMs for Verifying Technical System Specifications Against Requirements.

Can LLMs Generate User Stories and Assess Their Quality? Exploring LLMs for Verifying Technical System Specifications Against Requirements

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:46:35.795347Z

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Observation 0c9cd560-64e0-4a23-88fe-74e4f22915f7 · outbound

This paper cites Formal requirements engineering and large language models: A two-way roadmap,.

Can LLMs Generate User Stories and Assess Their Quality? Formal requirements engineering and large language models: A two-way roadmap,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.221135Z

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Observation f2069608-eb50-4ac4-a139-1e2fc4e51a14 · outbound

This paper cites Prompting Creative Requirements via Traceable and Adversarial Examples in Deep Learning,.

Can LLMs Generate User Stories and Assess Their Quality? Prompting Creative Requirements via Traceable and Adversarial Examples in Deep Learning,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.207164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4c832633-fa1a-4813-addb-a6c304237002 · outbound

This paper cites Exploring the Efficacy of ChatGPT in Generating Requirements: An Experimental Study,.

Can LLMs Generate User Stories and Assess Their Quality? Exploring the Efficacy of ChatGPT in Generating Requirements: An Experimental Study,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.191953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f020ba54-3c99-4233-a594-0d0d90f1bec2 · outbound

This paper cites Investigating ChatGPT’s Po- tential to Assist in Requirements Elicitation Processes,.

Can LLMs Generate User Stories and Assess Their Quality? Investigating ChatGPT’s Po- tential to Assist in Requirements Elicitation Processes,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.177604Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ada66b96-94ee-4025-a03d-b418578b8784 · outbound

This paper cites Using llms in software requirements specifications: An empirical evaluation,.

Can LLMs Generate User Stories and Assess Their Quality? Using llms in software requirements specifications: An empirical evaluation,

Reference 51

Resolution: verified exact; raw_fallback, observed 2026-08-06T15:46:35.967720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0a4093d7-d04e-4310-9506-568c888a32e8 · outbound

This paper cites Engineering safety requirements for autonomous driving with large language models,.

Can LLMs Generate User Stories and Assess Their Quality? Engineering safety requirements for autonomous driving with large language models,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.163364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c8f9f0aa-8ce7-4188-9690-6247429d927e · outbound

This paper cites Improving User Story Practice with the Grimm Method: A Multiple Case Study in the Software Industry,.

Can LLMs Generate User Stories and Assess Their Quality? Improving User Story Practice with the Grimm Method: A Multiple Case Study in the Software Industry,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.148846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fd2dd893-473a-43f6-b18d-19932b18ba06 · outbound

This paper cites Evaluating the Impact of User Stories Quality on the Ability to Understand and Structure Requirements,.

Can LLMs Generate User Stories and Assess Their Quality? Evaluating the Impact of User Stories Quality on the Ability to Understand and Structure Requirements,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.134478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bca686ba-16ef-4dca-b82d-43a0e4562eff · outbound

This paper cites Crowd- Based Requirements Elicitation via Pull Feedback: Method and Case Studies,.

Can LLMs Generate User Stories and Assess Their Quality? Crowd- Based Requirements Elicitation via Pull Feedback: Method and Case Studies,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:46:36.119015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 69c970b5-eb2c-411b-a7e2-1ec6205f1140 · outbound

This paper cites Available: https://doi.org/10.1007/s00766-022-00384-6.

Can LLMs Generate User Stories and Assess Their Quality? Available: https://doi.org/10.1007/s00766-022-00384-6

Reference 2022

Resolution: verified exact; doi, observed 2026-08-06T15:46:35.760214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation eb191137-1a1a-40f4-a8b5-e021e4ba6d72 · inbound

Automated Alignment between Elicitation Interviews and Requirements cites this paper.

Automated Alignment between Elicitation Interviews and Requirements Can LLMs Generate User Stories and Assess Their Quality?

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-04T11:13:33.637414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.