Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software | Paper Citation Record

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This paper cites Analysis of user comments: An approach for software requirements evolution,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Analysis of user comments: An approach for software requirements evolution,

Reference 1

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Observation 594ad67a-8405-48d6-8292-37ab87ab000c · outbound

This paper cites Fame: supporting continuous requirements elicitation by combining user feedback and monitoring,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Fame: supporting continuous requirements elicitation by combining user feedback and monitoring,

Reference 2

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This paper cites Re-swot: From user feedback to require- ments via competitor analysis,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Re-swot: From user feedback to require- ments via competitor analysis,

Reference 3

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This paper cites What would users change in my app? summarizing app reviews for recommending software changes,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software What would users change in my app? summarizing app reviews for recommending software changes,

Reference 4

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This paper cites Bug report, feature request, or simply praise? on automatically classifying app reviews,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Bug report, feature request, or simply praise? on automatically classifying app reviews,

Reference 5

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This paper cites Identification and classification of requirements from app user reviews.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Identification and classification of requirements from app user reviews

Reference 6

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Observation dac45f95-fabd-4d7e-bd8a-e13fd3a5375d · outbound

This paper cites From contract drafting to software specification: Linguistic sources of ambiguity,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software From contract drafting to software specification: Linguistic sources of ambiguity,

Reference 7

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Observation 3391797b-58ea-4a1b-85de-35815bd6ad1b · outbound

This paper cites On the interplay between consistency, completeness, and correctness in requirements evolution,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software On the interplay between consistency, completeness, and correctness in requirements evolution,

Reference 8

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This paper cites Naming the pain in requirements engineering: Contemporary problems, causes, and effects in practice,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Naming the pain in requirements engineering: Contemporary problems, causes, and effects in practice,

Reference 9

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Observation 0e7f37fa-de25-4e82-afb2-e251f4fb2bcf · outbound

This paper cites Software defect reduction top 10 list,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Software defect reduction top 10 list,

Reference 10

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This paper cites From ideas to expressed needs: an empirical study on the evolution of requirements during elicitation,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software From ideas to expressed needs: an empirical study on the evolution of requirements during elicitation,

Reference 11

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This paper cites The prevalence and severity of persistent ambiguity in software requirements specifications: Is a special effort needed to find them?.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software The prevalence and severity of persistent ambiguity in software requirements specifications: Is a special effort needed to find them?

Reference 12

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This paper cites Two case studies of open source software development: Apache and mozilla,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Two case studies of open source software development: Apache and mozilla,

Reference 13

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This paper cites How do open source software (oss) developers practice and perceive requirements engineering? an empir- ical study,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software How do open source software (oss) developers practice and perceive requirements engineering? an empir- ical study,

Reference 14

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This paper cites Free/libre open- source software development: What we know and what we do not know,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Free/libre open- source software development: What we know and what we do not know,

Reference 15

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Observation 634d0411-cec7-468c-806f-35c609664ecb · outbound

This paper cites A framework analysis of the open source development paradigm, 2000.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software A framework analysis of the open source development paradigm, 2000

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.849760Z

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This paper cites Motivation, governance, and the viability of hybrid forms in open source software development,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Motivation, governance, and the viability of hybrid forms in open source software development,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.841142Z

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This paper cites The social structure of free and open source software development,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software The social structure of free and open source software development,

Reference 18

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This paper cites Inquiry-based requirements analysis,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Inquiry-based requirements analysis,

Reference 19

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Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Data and resources,

Reference 20

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This paper cites Attention is all you need,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Attention is all you need,

Reference 21

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This paper cites A survey on evaluation of large language models,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software A survey on evaluation of large language models,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.806041Z

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This paper cites A Survey of Large Language Models.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software A Survey of Large Language Models

Reference 23

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Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 24

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This paper cites Large language models for software engi- neering: A systematic literature review,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Large language models for software engi- neering: A systematic literature review,

Reference 25

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Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Pre- train, prompt, and predict: A systematic survey of prompting methods in natural language processing,

Reference 26

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Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Language models are unsupervised multitask learners,

Reference 27

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Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks

Reference 28

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Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Training language models to follow instructions with human feedback

Reference 29

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Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Language mod- els are few-shot learners,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.771182Z

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This paper cites A Survey on In-context Learning.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software A Survey on In-context Learning

Reference 31

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Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software True few-shot learning with language models,

Reference 32

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Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software C-ICL: Contrastive In-context Learning for Information Extraction

Reference 33

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Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software In-context Example Selection with Influences

Reference 34

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Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Requirements Satisfiability with In-Context Learning

Reference 35

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This paper cites Pohl, Requirements engineering: An overview.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Pohl, Requirements engineering: An overview

Reference 36

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Observation 90353bd3-6cce-403b-883e-cf0e96c5c62c · outbound

This paper cites Using domain-specific corpora for improved handling of ambiguity in requirements,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Using domain-specific corpora for improved handling of ambiguity in requirements,

Reference 37

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This paper cites Ambiguity in requirements specification,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Ambiguity in requirements specification,

Reference 38

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Observation 947e3e5f-c92d-45c8-8883-c8029b5f84dd · outbound

This paper cites Verifying and validating software requirements and design specifications,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Verifying and validating software requirements and design specifications,

Reference 39

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This paper cites Ambiguity in Natural Language Software Requirements: A Case Study,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Ambiguity in Natural Language Software Requirements: A Case Study,

Reference 40

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Observation fa46436a-eb5a-46e2-9b52-9c9a930c971b · outbound

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Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Unresolved cited work

Reference 41

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Observation bdcfcbd1-9569-499f-9dc5-5ba7286fe239 · outbound

This paper cites Requirement ambiguity not as important as expected—results of an empirical evaluation,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Requirement ambiguity not as important as expected—results of an empirical evaluation,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.699794Z

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Observation cce9e40e-1ff3-4c33-bb83-f698a983e28b · outbound

This paper cites A framework for quality assessment of just-in-time requirements: the case of open source feature requests,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software A framework for quality assessment of just-in-time requirements: the case of open source feature requests,

Reference 43

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This paper cites Rule-based nlp vs chatgpt in ambiguity detection, a preliminary study,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Rule-based nlp vs chatgpt in ambiguity detection, a preliminary study,

Reference 44

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This paper cites Automated handling of anaphoric ambiguity in requirements: A multi-solution study,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Automated handling of anaphoric ambiguity in requirements: A multi-solution study,

Reference 45

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This paper cites Addressing lexical and semantic ambiguity in natural language requirements,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Addressing lexical and semantic ambiguity in natural language requirements,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.653140Z

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Observation d711839d-5d92-4177-a1b1-17e3934615a0 · outbound

This paper cites Rapid quality assurance with requirements smells,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Rapid quality assurance with requirements smells,

Reference 47

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This paper cites Detecting requirements defects with nlp patterns: an industrial experience in the railway domain,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Detecting requirements defects with nlp patterns: an industrial experience in the railway domain,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.634754Z

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Observation b12ae0fb-cce0-44ff-bdab-4c70b053be21 · outbound

This paper cites Automatic detection of nocuous coordination ambiguities in natural language requirements,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Automatic detection of nocuous coordination ambiguities in natural language requirements,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.623005Z

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Observation 8f457a61-9cfb-4ae5-beca-1427a20df10d · outbound

This paper cites Ambiguity detection: Towards a tool explaining ambiguity sources,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Ambiguity detection: Towards a tool explaining ambiguity sources,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.612385Z

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Observation 4e009d21-4733-443b-beed-ed3940b78691 · outbound

This paper cites Nero: A text-based tool for content annotation and detection of smells in feature requests,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Nero: A text-based tool for content annotation and detection of smells in feature requests,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.601479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 387467e9-c6de-44d6-afa0-b75c68efa755 · outbound

This paper cites Detecting bad smells in use case de- scriptions,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Detecting bad smells in use case de- scriptions,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.589647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5fbd25fe-b043-4ab3-b600-2617a7494d79 · outbound

This paper cites An empirical study on the potential usefulness of domain models for completeness checking of requirements,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software An empirical study on the potential usefulness of domain models for completeness checking of requirements,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.577792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 39e594ad-5acc-4014-a66c-0146033cd61c · outbound

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

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Improving requirements completeness: Automated assistance through large language models,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T16:26:35.120445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb8d5541-fa9d-4e55-be3d-b220e601e5f7 · outbound

This paper cites Reqcompletion: Domain-enhanced automatic completion for software requirements,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Reqcompletion: Domain-enhanced automatic completion for software requirements,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.559599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7c25f9c8-8810-4a39-8363-62178054ab4b · outbound

This paper cites Automated smell detection and recommendation in natural language requirements,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Automated smell detection and recommendation in natural language requirements,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.549687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 597ae7d3-c3ad-4333-b866-e889f81ff3a3 · outbound

This paper cites Free/libre open-source software development: What we know and what we do not know,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Free/libre open-source software development: What we know and what we do not know,

Reference 57

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T16:26:35.337104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7b10267c-fb44-4042-9455-46bc50b97f39 · outbound

This paper cites An analysis of requirements evolution in open source projects: Recommendations for issue trackers,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software An analysis of requirements evolution in open source projects: Recommendations for issue trackers,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.538333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 767e897c-c529-4264-87a9-0dfca9189798 · outbound

This paper cites Towards utility-based prioritization of requirements in open source environments,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Towards utility-based prioritization of requirements in open source environments,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.528659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 526d1280-c089-433f-ade6-3254fc70a87c · outbound

This paper cites How do open source software (oss) developers practice and perceive requirements engineering? an empir- ical study,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software How do open source software (oss) developers practice and perceive requirements engineering? an empir- ical study,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.517055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b8e0a894-4fb7-4072-84ad-dfdc00bbe0fb · outbound

This paper cites Social networking meets software development: Perspectives from github, msdn, stack exchange, and topcoder,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Social networking meets software development: Perspectives from github, msdn, stack exchange, and topcoder,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.502757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7761a3f2-2fb7-45b1-b3f5-5e42fb2a8318 · outbound

This paper cites Using bug descriptions to reformulate queries during text-retrieval-based bug localization,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Using bug descriptions to reformulate queries during text-retrieval-based bug localization,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.494481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7e4e2bb8-4b5a-4c4a-b23c-b50fc66b0746 · outbound

This paper cites Combining Language and App UI Analysis for the Automated Assessment of Bug Reproduction Steps.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Combining Language and App UI Analysis for the Automated Assessment of Bug Reproduction Steps

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T16:26:35.144094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c57383ef-a7cc-48d1-ba5f-5c49bc2cbc0c · outbound

This paper cites Who should fix this bug?.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Who should fix this bug?

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-06T16:26:35.146935Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d2f868d-ac71-44d3-9561-c0d6b7e56b34 · outbound

This paper cites What makes a good bug report?.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software What makes a good bug report?

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.479829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0687f05b-c9c6-41f2-a61e-fdd0a3b4e6c6 · outbound

This paper cites SWE-bench: Can Language Models Resolve Real-World GitHub Issues?.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software SWE-bench: Can Language Models Resolve Real-World GitHub Issues?

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-06T16:26:35.152325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8bc6904f-a4f4-4169-8a42-b6a84b0036ad · outbound

This paper cites Basics of qualitative research: Techniques and procedures for developing grounded theory.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Basics of qualitative research: Techniques and procedures for developing grounded theory

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.471593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d557eb38-f21f-41a5-a6f5-e189546958ce · outbound

This paper cites A coefficient of agreement for nominal scales,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software A coefficient of agreement for nominal scales,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.463362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b2264163-ab11-4265-8c81-ff159b5bd5e8 · outbound

This paper cites Model generation with LLMs: From requirements to UML sequence diagrams,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Model generation with LLMs: From requirements to UML sequence diagrams,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.455525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3d40e08c-2e4c-46b3-8e0e-5f0da36aabd2 · outbound

This paper cites Scaling instruction-finetuned language models,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Scaling instruction-finetuned language models,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.447409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6b07d13e-f0eb-414c-94cb-7b96fc46cbd8 · outbound

This paper cites Training Compute-Optimal Large Language Models.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Training Compute-Optimal Large Language Models

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-06T16:26:35.167761Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1edf88a2-f225-42b1-b5cb-0ccbc061fdb6 · outbound

This paper cites CLAM: Selective Clarification for Ambiguous Questions with Generative Language Models.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software CLAM: Selective Clarification for Ambiguous Questions with Generative Language Models

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-06T16:26:35.170662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0411a2bd-ff95-4620-a19f-fe449e31d775 · outbound

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

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Survey of hallucination in natural language generation,

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-06T16:26:35.173429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39918013-5209-4665-9f0b-2a09126e47cc · outbound

This paper cites Revisiting automatic evalu- ation of extractive summarization task: Can we do better than rouge?.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Revisiting automatic evalu- ation of extractive summarization task: Can we do better than rouge?

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.432643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3852d754-48af-43a6-a4ae-cce13971677c · outbound

This paper cites Toward regulatory compliance: A few-shot learning approach to extract processing activities,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Toward regulatory compliance: A few-shot learning approach to extract processing activities,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.424011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b141fd6b-910f-477c-a71f-65d8fc7a59c9 · outbound

This paper cites A large language model approach to code and privacy policy alignment,.

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software A large language model approach to code and privacy policy alignment,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.415643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b64438d2-5bb3-40aa-8fe6-0d9661376eb6 · outbound

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

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software Chain-of-thought prompting elicits reasoning in large language models,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:26:35.407262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Reference resolution

Outbound references

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