Paper Citation Record · LEDGER

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models

As of 22 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2505.03265.

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

pith.paper-citation-record.v1
2505.03265 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:59:12.475245Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

31 of 31 outbound references displayed

verified exact3
verified fuzzy14
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 454ce293-768f-4e82-994d-0f218fef56ce · outbound

This paper cites The state-of-practice in require- ments specification: an extended interview study at 12 companies.

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models The state-of-practice in require- ments specification: an extended interview study at 12 companies

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T23:59:12.360501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ae80e22-5223-48ac-a984-82a94146cddb · outbound

This paper cites DeepSeek-V3 Technical Report.

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models DeepSeek-V3 Technical Report

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T23:59:12.365131Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88df4f0a-0308-4fd6-9d73-a2069add83ba · outbound

This paper cites Design science as nested problem solving.

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models Design science as nested problem solving

Reference 3

Resolution: metadata mismatch; raw_fallback, observed 2026-08-15T23:59:12.852581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d780f2eb-d3ac-439b-abc6-d162f68aeb1d · outbound

This paper cites Data Augmentation for Conflict and Duplicate Detection in Software Engineering Sentence Pairs.

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models Data Augmentation for Conflict and Duplicate Detection in Software Engineering Sentence Pairs

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:59:12.782643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8ddf912a-64ef-4344-9f75-d5f07558d53c · outbound

This paper cites Multi-type requirements traceability prediction by code data augmentation and fine-tuning MS-CodeBERT.

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models Multi-type requirements traceability prediction by code data augmentation and fine-tuning MS-CodeBERT

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T23:59:12.376996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e75772de-3350-4b6f-a485-307971c7f132 · outbound

This paper cites EfficientExtractionofTechnicalRequirementsApplying Data Augmentation.

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models EfficientExtractionofTechnicalRequirementsApplying Data Augmentation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T23:59:12.380849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ddeb7a6-2b10-48c1-91e6-d035fcb86e34 · outbound

This paper cites Language Models are Few-Shot Learners.

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models Language Models are Few-Shot Learners

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T23:59:12.385094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 184410ce-009f-4dec-a59e-565476263203 · outbound

This paper cites Few-shot fine-tuning vs. in-context learning: A fair comparison and evaluation.

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models Few-shot fine-tuning vs. in-context learning: A fair comparison and evaluation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:59:13.067492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 72b4f816-73d0-4480-bd25-0d1eb917ad86 · outbound

This paper cites Natural Language Processing for Requirements Engineering: A Systematic Mapping Study.

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models Natural Language Processing for Requirements Engineering: A Systematic Mapping Study

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:59:13.054777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e5721dd1-c37e-4b70-af08-e6fa4c9cee40 · outbound

This paper cites Machine learning for requirements engineering (ML4RE): A systematic literature review complemented by practitioners’ voices from Stack Overflow.

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models Machine learning for requirements engineering (ML4RE): A systematic literature review complemented by practitioners’ voices from Stack Overflow

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:59:13.042583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c4e0fc2a-c97c-4290-9e32-78a235bdc1c7 · outbound

This paper cites Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations.

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:59:13.030257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ba2ea137-d2bf-466f-a114-cfb9f57cdeb1 · outbound

This paper cites ChatGPT outperforms crowd workers for text-annotation tasks.

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models ChatGPT outperforms crowd workers for text-annotation tasks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:59:13.017880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 447994e4-bca2-45d1-99e4-4788cf74e38a · outbound

This paper cites ZeroGen: Efficient Zero-shot Learning via Dataset Generation.

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models ZeroGen: Efficient Zero-shot Learning via Dataset Generation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T23:59:12.408190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 166ef1c9-c26d-4673-ad75-efa0262ac122 · outbound

This paper cites On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey.

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T23:59:12.412170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2afb829-6e15-43d1-93dc-724fb719a186 · outbound

This paper cites Which AI Technique Is Better to Classify Requirements? An Experiment with SVM, LSTM, and ChatGPT.

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models Which AI Technique Is Better to Classify Requirements? An Experiment with SVM, LSTM, and ChatGPT

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:59:12.569442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 38d78fdb-d4b1-441d-b293-93e1bda8da0f · outbound

This paper cites PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees.

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:59:13.006169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 63eb4a28-6d6b-480c-a4aa-d09b17736daf · outbound

This paper cites Replication in Requirements Engineering: the NLP for RE Case.

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models Replication in Requirements Engineering: the NLP for RE Case

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:59:12.553354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 20cdd47f-a35c-40c9-a409-b0448f696754 · outbound

This paper cites Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models.

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T23:59:12.427599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e777a550-3f60-40b7-83f3-45069cc3cb32 · outbound

This paper cites Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias.

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:59:12.993770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 857a67c9-d211-4a78-8f9b-15119309d76c · outbound

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

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models BERT: Pre-training of Deep Bidirectional Trans- formers for Language Understanding

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T23:59:12.435088Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fef5b97c-2b82-423e-8137-e2004a4fc2be · outbound

This paper cites an unresolved cited work.

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-15T23:59:12.981759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation de3f6b16-792f-44fd-8b0c-3aaefa86d540 · outbound

This paper cites an unresolved cited work.

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-15T23:59:12.970209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b5e8bc7b-9a85-4d08-992b-1453bf729f11 · outbound

This paper cites Software product lines essentials.

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models Software product lines essentials

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:59:12.957285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1b06e5e7-0468-4893-9c21-594d8a0ba268 · outbound

This paper cites Preventing Requirement Defects: An Experiment in Process Improvement.

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models Preventing Requirement Defects: An Experiment in Process Improvement

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:59:12.945312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cfb359b5-5d28-46f2-b6b3-9e796166a780 · outbound

This paper cites Lessons from the Use of Natural Language Inference (NLI) in Requirements Engineering Tasks.

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models Lessons from the Use of Natural Language Inference (NLI) in Requirements Engineering Tasks

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:59:12.931889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ced2332b-c5bb-4e1c-840f-b73923bd4c66 · outbound

This paper cites SDP-BB: A Software Defect Prediction Model Using BiLSTM and BERT-Based Se- mantic Features.

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models SDP-BB: A Software Defect Prediction Model Using BiLSTM and BERT-Based Se- mantic Features

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:59:12.917626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation beb0eea6-df4e-44f2-84fa-e0a29f2e1b67 · outbound

This paper cites Automated Quality Defect Detection in Software Development Documents.

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models Automated Quality Defect Detection in Software Development Documents

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:59:12.904096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fa62cd2a-66be-4073-a7b9-39d2adde9183 · outbound

This paper cites Ambiguity in Requirements Specification.

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models Ambiguity in Requirements Specification

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:59:12.891196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0d1e1319-f67c-42f5-b021-9e51808609b0 · outbound

This paper cites Instruction Tuning with GPT-4.

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models Instruction Tuning with GPT-4

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T23:59:12.467945Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f995f775-0bc7-4f7a-806e-29b08416c804 · outbound

This paper cites True Few-Shot Learning with Language Models.

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models True Few-Shot Learning with Language Models

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:59:12.878613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation df6a7186-ef94-4d16-a27b-d20ba27660b8 · outbound

This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T23:59:12.475245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.