Paper Citation Record · LEDGER

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation

As of 15 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 4 inbound Pith citation observations for arXiv:2411.14971.

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

pith.paper-citation-record.v1
2411.14971 v1

Coverage vector

measured 65 of 65 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:44:21.169965Z

measured 69 of 69 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:10:13.178316Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

65 of 65 outbound references displayed

verified exact2
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External citation measurements

3
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 8680de64-4886-43a0-a488-ec338ba529e1 · outbound

This paper cites Information Technology: Agencies need to develop modernization plans for critical legacy sys- tems,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Information Technology: Agencies need to develop modernization plans for critical legacy sys- tems,

Reference 1

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This paper cites The legacy problem in government agencies: An exploratory study,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation The legacy problem in government agencies: An exploratory study,

Reference 2

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Observation e4217b14-6077-49ed-9c10-a4279288a6b5 · outbound

This paper cites Federal IT Modernization Needs a Strategy and More Money,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Federal IT Modernization Needs a Strategy and More Money,

Reference 3

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This paper cites The future of GenAI will rocket fuel modernisation of core legacy systems,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation The future of GenAI will rocket fuel modernisation of core legacy systems,

Reference 4

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This paper cites Ai-powered application rewrite: Revolutionizing legacy code transformation,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Ai-powered application rewrite: Revolutionizing legacy code transformation,

Reference 5

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Observation cccb3e93-8454-4972-8800-ca657914a3f4 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Evaluating Large Language Models Trained on Code

Reference 6

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This paper cites AI rewrites coding,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation AI rewrites coding,

Reference 7

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This paper cites Large language models for software engineering: Sur- vey and open problems,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Large language models for software engineering: Sur- vey and open problems,

Reference 8

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Observation 5c2f1f49-ebc5-4325-9616-2360ee32061c · outbound

This paper cites Neural Machine Translation for Code Generation.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Neural Machine Translation for Code Generation

Reference 9

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This paper cites Large language models and simple, stupid bugs,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Large language models and simple, stupid bugs,

Reference 10

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This paper cites Is Stack Overflow obsolete? An empirical study of the characteristics of ChatGPT answers to Stack Overflow questions,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Is Stack Overflow obsolete? An empirical study of the characteristics of ChatGPT answers to Stack Overflow questions,

Reference 11

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This paper cites Expectation vs. experi- ence: Evaluating the usability of code generation tools powered by large language models,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Expectation vs. experi- ence: Evaluating the usability of code generation tools powered by large language models,

Reference 12

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This paper cites Better together? an evaluation of ai-supported code translation,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Better together? an evaluation of ai-supported code translation,

Reference 13

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This paper cites Bridging Eras: Transforming Fortran legacies into Python with the power of large language models,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Bridging Eras: Transforming Fortran legacies into Python with the power of large language models,

Reference 14

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Observation 780bcf04-1d8f-44e7-821a-51798b461b36 · outbound

This paper cites A neural model for gener- ating natural language summaries of program subroutines,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation A neural model for gener- ating natural language summaries of program subroutines,

Reference 15

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This paper cites Improved code summarization via a graph neural network,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Improved code summarization via a graph neural network,

Reference 16

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This paper cites Improving automatic source code summarization via deep reinforcement learning,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Improving automatic source code summarization via deep reinforcement learning,

Reference 17

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This paper cites Recommendations for datasets for source code summarization,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Recommendations for datasets for source code summarization,

Reference 18

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This paper cites The evolution of a language standard: MUMPS in the 1980s,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation The evolution of a language standard: MUMPS in the 1980s,

Reference 19

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Observation 512f19a0-7bfa-4b0a-b27e-eb569cb62896 · outbound

This paper cites A human study of comprehension and code summariza- tion,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation A human study of comprehension and code summariza- tion,

Reference 20

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This paper cites Information technology: Agencies need to continue addressing critical legacy systems,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Information technology: Agencies need to continue addressing critical legacy systems,

Reference 21

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This paper cites Information Technology: Federal agencies need to address aging legacy systems,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Information Technology: Federal agencies need to address aging legacy systems,

Reference 22

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This paper cites Information technology: IRS needs to complete modernization plans and fully address cloud comput- ing requirements,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Information technology: IRS needs to complete modernization plans and fully address cloud comput- ing requirements,

Reference 23

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Observation c15d24f5-4fda-4063-bb65-6443466b51f9 · outbound

This paper cites Chapter 12 - Veterans Health Admin- istration’s VistA MUMPS modernization pilot**© 2010. The Software Revolution, Inc. all rights reserved.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Chapter 12 - Veterans Health Admin- istration’s VistA MUMPS modernization pilot**© 2010. The Software Revolution, Inc. all rights reserved

Reference 24

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Observation f48258c6-1f85-4e01-b2d7-bddaa2ea31be · outbound

This paper cites Open challenges in incremental coverage of legacy software languages,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Open challenges in incremental coverage of legacy software languages,

Reference 25

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Observation bced670c-eed3-471a-b52a-b7604dd36e25 · outbound

This paper cites Verified Code Transpilation with LLMs.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Verified Code Transpilation with LLMs

Reference 26

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This paper cites Lost in translation: A study of bugs introduced by large language models while translating code,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Lost in translation: A study of bugs introduced by large language models while translating code,

Reference 27

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This paper cites Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks

Reference 28

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This paper cites Neutron: an attention-based neural decompiler,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Neutron: an attention-based neural decompiler,

Reference 29

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This paper cites A Neural-based Program Decompiler.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation A Neural-based Program Decompiler

Reference 30

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Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Towards Neural Decompilation

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This paper cites Automatic source code summa- rization of context for Java methods,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Automatic source code summa- rization of context for Java methods,

Reference 32

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Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Automatic generation of natural language summaries for Java classes,

Reference 33

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Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Towards automatically generating summary comments for Java meth- ods,

Reference 34

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Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation CloCom: Mining existing source code for automatic comment generation,

Reference 35

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This paper cites A survey of automatic source code summarization,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation A survey of automatic source code summarization,

Reference 36

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This paper cites Automatic Code Summarization: A Systematic Literature Review.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Automatic Code Summarization: A Systematic Literature Review

Reference 37

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This paper cites Improving code summarization with block-wise abstract syntax tree splitting,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Improving code summarization with block-wise abstract syntax tree splitting,

Reference 38

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This paper cites Deep code comment generation with hybrid lexical and syntactical information,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Deep code comment generation with hybrid lexical and syntactical information,

Reference 39

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This paper cites Summarizing source code using a neural attention model,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Summarizing source code using a neural attention model,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:44:21.576839Z

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This paper cites Code structure–Guided transformer for source code summarization,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Code structure–Guided transformer for source code summarization,

Reference 41

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This paper cites On the Opportunities and Risks of Foundation Models.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation On the Opportunities and Risks of Foundation Models

Reference 42

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Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Assemble foundation models for automatic code summarization,

Reference 43

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Observation e3dcf23e-edd6-46a7-8452-deb7f732c80c · outbound

This paper cites Automatic code documentation generation using GPT-3,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Automatic code documentation generation using GPT-3,

Reference 44

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Observation 2f888c0f-f50f-4ce5-bb92-5438f0e8ed32 · outbound

This paper cites A comparative analysis of large language models for code documentation generation,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation A comparative analysis of large language models for code documentation generation,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:44:21.529420Z

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Observation dfb6d741-b75b-45d1-8b05-ab3a6db7be16 · outbound

This paper cites Comparing code explanations created by students and large language models,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Comparing code explanations created by students and large language models,

Reference 46

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Observation 6ac9017f-c36c-453d-9a93-445c8909a127 · outbound

This paper cites Using an LLM to help with code understanding,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Using an LLM to help with code understanding,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:44:21.506891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0059ac64-2999-4c0c-8bb6-41a5c51f6cdf · outbound

This paper cites Code summarization: Do transformers really understand code?.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Code summarization: Do transformers really understand code?

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:44:21.496132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a14790ca-9473-4c88-900d-2d0c52e8531b · outbound

This paper cites Testing the effect of code documentation on large language model code understanding,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Testing the effect of code documentation on large language model code understanding,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:44:21.484805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 70238c32-0702-41cd-9306-a10a727670f6 · outbound

This paper cites Evaluating software documentation quality,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Evaluating software documentation quality,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:44:21.474168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0e4c0e02-f7cd-4f2a-98cc-8625e249d3ad · outbound

This paper cites Correlating automated and human evaluation of code documentation generation quality,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Correlating automated and human evaluation of code documentation generation quality,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:44:21.463886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 93802e57-3583-4aa2-9870-e20cdf768cba · outbound

This paper cites On the evaluation of neural code summarization,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation On the evaluation of neural code summarization,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:44:21.452186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a9c1ddd3-3c81-43b5-a297-886aeb003532 · outbound

This paper cites ”We Need Structured Output.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation ”We Need Structured Output

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:44:21.441086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b8343d5e-a46f-49a2-a2d7-355939c3ff4c · outbound

This paper cites Label Studio: Data labeling software,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Label Studio: Data labeling software,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-12T14:44:21.128853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f14101a-4bfb-494b-80ca-478721ab6adb · outbound

This paper cites ROUGE: A package for automatic evaluation of summaries,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation ROUGE: A package for automatic evaluation of summaries,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:44:21.421694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8c827059-321e-4875-a3bd-3569d5f5781f · outbound

This paper cites chrF: Character n-gram F-score for automatic MT eval- uation,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation chrF: Character n-gram F-score for automatic MT eval- uation,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:44:21.410203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 264c6f66-7e9e-44b8-869b-9948a04a29f7 · outbound

This paper cites BLEU: A method for automatic evaluation of machine translation,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation BLEU: A method for automatic evaluation of machine translation,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:44:21.396270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 820a1ee8-aea6-4429-bf21-15ccb135a020 · outbound

This paper cites A guideline of selecting and reporting intraclass correlation coefficients for reliability research,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation A guideline of selecting and reporting intraclass correlation coefficients for reliability research,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:44:21.383373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5bd37410-ec28-4095-bbe4-d108907b6780 · outbound

This paper cites Practitioners’ expectations on automated code comment generation,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Practitioners’ expectations on automated code comment generation,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:44:21.371257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9d5e1444-ae91-44aa-b5e1-85dca59da090 · outbound

This paper cites Leveraging Large Language Models for NLG Evaluation: Advances and Challenges.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Leveraging Large Language Models for NLG Evaluation: Advances and Challenges

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-12T14:44:21.151093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c86f1bb1-a63b-44f1-88b7-00e0dbdd785d · outbound

This paper cites Ibm unveils watsonx generative AI capabilities to accelerate mainframe application modernization,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Ibm unveils watsonx generative AI capabilities to accelerate mainframe application modernization,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:44:21.359505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b0e182cd-9767-4db4-8f8d-691c95f0d5c3 · outbound

This paper cites Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-12T14:44:21.163475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44214d4b-da48-4f29-b8f5-cb3cf10dd57b · outbound

This paper cites Semantic similarity metrics for evaluating source code summarization,.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Semantic similarity metrics for evaluating source code summarization,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:44:21.337109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 109ed7d5-bd23-496a-b7b2-bf823bbb3fb8 · outbound

This paper cites Evaluating Code Summarization Techniques: A New Metric and an Empirical Characterization.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Evaluating Code Summarization Techniques: A New Metric and an Empirical Characterization

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-12T14:44:21.169965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7a48192-225f-49b8-a231-39ccd6fb0d74 · outbound

This paper cites Available: https://newsroom.ibm.com/ 2023-08-22-IBM-Unveils-watsonx-Generative-AI-Capabilities-to-Accelerate-Mainframe-Application-Modernization.

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation Available: https://newsroom.ibm.com/ 2023-08-22-IBM-Unveils-watsonx-Generative-AI-Capabilities-to-Accelerate-Mainframe-Application-Modernization

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:44:21.348237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation e1dc4ed0-5404-4edc-833f-eb3350b6f6ed · inbound

Can LLMs Replace Humans During Code Chunking? cites this paper.

Can LLMs Replace Humans During Code Chunking? Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:13.178316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f742bfd8-15e6-46a1-bc8c-2d321677fe06 · inbound

When control meets large language models: From words to dynamics cites this paper.

When control meets large language models: From words to dynamics Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation

Reference 70

Resolution: verified exact; arxiv_id, observed 2026-05-21T14:54:13.173017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 46558c09-6aad-4399-8471-22b53900795f · inbound

Reversa: A Reverse Documentation Engineering Framework for Converting Legacy Software into Operational Specifications for AI Agents cites this paper.

Reversa: A Reverse Documentation Engineering Framework for Converting Legacy Software into Operational Specifications for AI Agents Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T09:03:09.744909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation beb1165c-ca46-4786-8f37-96514e5c9ce4 · inbound

Articulate but Wrong: Self-Review Failures in LLM-Based Code Modernization cites this paper.

Articulate but Wrong: Self-Review Failures in LLM-Based Code Modernization Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-22T01:45:54.879469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.