Paper Citation Record · LEDGER

A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering

As of 19 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2411.09974.

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

pith.paper-citation-record.v1
2411.09974 v2

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T20:08:36.610380Z

measured 26 of 26 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 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

26 of 26 outbound references displayed

verified exact0
verified fuzzy10
unresolved16
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f7d86acd-bf4c-4a60-adb3-38ccba369603 · outbound

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

A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering Using an llm to help with code understanding,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T20:08:36.093411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebd9e97f-7ebf-448b-9b4a-fce0c73ab6f8 · outbound

This paper cites Uncovering the causes of emotions in software developer communication using zero-shot llms,.

A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering Uncovering the causes of emotions in software developer communication using zero-shot llms,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T20:08:36.164465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9508f013-c92b-434a-92bf-aaf253b32b51 · outbound

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

A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering Large language models for software engi- neering: A systematic literature review,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T20:08:36.167872Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46e0aed4-e2d5-44da-820f-7da4680d77a6 · outbound

This paper cites Chatgpt prompt patterns for improving code quality, refactoring, requirements elicitation, and software design,.

A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering Chatgpt prompt patterns for improving code quality, refactoring, requirements elicitation, and software design,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:08:37.042454Z

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 56fa8710-ddc3-452c-ac46-b3e4666bf0df · outbound

This paper cites Is ChatGPT the Ultimate Programming Assistant -- How far is it?.

A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering Is ChatGPT the Ultimate Programming Assistant -- How far is it?

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T20:08:36.174845Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f7aa11e-fb19-4cf6-a7e2-b298f34b1b40 · outbound

This paper cites Using large language models to support software engineering documentation in waterfall life cycles: Are we there yet?.

A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering Using large language models to support software engineering documentation in waterfall life cycles: Are we there yet?

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:08:36.999630Z

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 53a2a2c0-686f-4100-b83d-6d88375003e6 · outbound

This paper cites Unveiling chatgpt’s usage in open source projects: A mining-based study,.

A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering Unveiling chatgpt’s usage in open source projects: A mining-based study,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:08:36.990665Z

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 f2746d00-148b-4fc9-8b8c-f11408a998e6 · outbound

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

A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering Large language models for software engineering: Sur- vey and open problems,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T20:08:36.244315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d15d0bb6-cdff-4b35-9e7c-cf0a001f620a · outbound

This paper cites Do Developers Adopt Green Architectural Tactics for ML-Enabled Systems? A Mining Software Repository Study.

A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering Do Developers Adopt Green Architectural Tactics for ML-Enabled Systems? A Mining Software Repository Study

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-12T20:08:36.281804Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d00e827f-9a9d-424f-b482-b9cfee6ca78f · outbound

This paper cites How do machine learning models change?.

A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering How do machine learning models change?

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:08:36.975462Z

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 d6240602-86d8-4061-a4dc-c7e29dc1f2fe · outbound

This paper cites Detecting code smells using chatgpt: Initial insights,.

A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering Detecting code smells using chatgpt: Initial insights,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:08:36.892252Z

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 29234e1f-e4c3-4d8c-9e67-d00bb79f95ff · outbound

This paper cites Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code.

A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T20:08:36.292160Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f50c80c-950e-484b-a9f3-263b6d548353 · outbound

This paper cites Detecting code comment inconsistencies using llm and program analysis,.

A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering Detecting code comment inconsistencies using llm and program analysis,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T20:08:36.295231Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cfc51e01-3e74-4c0d-a2bf-6cb98d102a5d · outbound

This paper cites A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications.

A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T20:08:36.356991Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1790eaff-e71a-4201-9c43-3ab6e89df755 · outbound

This paper cites Finetuned Language Models Are Zero-Shot Learners.

A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering Finetuned Language Models Are Zero-Shot Learners

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T20:08:36.464630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb3b3336-0f2b-4f9e-a026-65115ed9d2bc · outbound

This paper cites Do Advanced Language Models Eliminate the Need for Prompt Engineering in Software Engineering?.

A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering Do Advanced Language Models Eliminate the Need for Prompt Engineering in Software Engineering?

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T20:08:36.468163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62b96086-1aab-41d5-bd77-cf901e7bfbac · outbound

This paper cites Language Models are Few-Shot Learners.

A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering Language Models are Few-Shot Learners

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T20:08:36.472181Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84efdca6-766c-4a08-8ea7-fda94a87e381 · outbound

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

A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering Chain-of-thought prompting elicits reasoning in large language models,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T20:08:36.474651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82c02e87-c8c5-4249-84b3-cf86dac3dfe8 · outbound

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

A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering A coefficient of agreement for nominal scales,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T20:08:36.508369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45e2a312-2ef3-45c5-913f-56fcd3449783 · outbound

This paper cites A Survey of Large Language Models.

A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering A Survey of Large Language Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T20:08:36.566649Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37acc60b-9cd3-4aaf-8276-aa80f4e6cc71 · outbound

This paper cites Sampling in software engineering research: A critical review and guidelines,.

A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering Sampling in software engineering research: A critical review and guidelines,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T20:08:36.584162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8c9791c-ee06-4d28-a408-287a49eb52be · outbound

This paper cites Chatgpt api keys,.

A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering Chatgpt api keys,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:08:36.813746Z

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 fd5f5af5-12f1-4266-84fd-3dba69d5c464 · outbound

This paper cites Getting started - anthropic,.

A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering Getting started - anthropic,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:08:36.805243Z

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 4c574ca8-689a-4997-8e8d-1ea6dda117ea · outbound

This paper cites A synthesis of green architectural tactics for ml-enabled systems,.

A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering A synthesis of green architectural tactics for ml-enabled systems,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:08:36.795957Z

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 eae3e4c2-f363-4099-874d-ca37f6ad6755 · outbound

This paper cites Home — Great Expectations — docs.greatexpectations.io,.

A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering Home — Great Expectations — docs.greatexpectations.io,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:08:36.751142Z

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 2f377265-5bbd-49b6-b940-382e1c6ef7b2 · outbound

This paper cites Teaching mining software repositories.

A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering Teaching mining software repositories

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:08:36.713174Z

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

No inbound Pith citation observations are available.