Paper Citation Record · LEDGER

Requirements Engineering for Machine Learning: Perspectives from Data Scientists

As of 21 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:1908.04674.

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

pith.paper-citation-record.v1
1908.04674 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:37:33.739307Z

measured 35 of 35 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

35 of 35 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5adaf66b-046b-4abf-b880-4f61a42b3e14 · outbound

This paper cites Software engineering for machine-learning applications: The road a head,.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists Software engineering for machine-learning applications: The road a head,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:37:34.140780Z

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 c7d3f364-96b1-48b9-b219-ae4b75e4387a · outbound

This paper cites How do engineers perceive d ifﬁculties in engineering of machine-learning systems? - Questionnai re survey,.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists How do engineers perceive d ifﬁculties in engineering of machine-learning systems? - Questionnai re survey,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:37:34.130243Z

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 db74980e-70f8-431a-9f9e-0122a5f302a8 · outbound

This paper cites Machine learning to guide performance testing: An autonom ous test framework,.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists Machine learning to guide performance testing: An autonom ous test framework,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:37:34.120503Z

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

Observation b08754a5-e909-4220-8847-0e98d2475008 · outbound

This paper cites Using reinforcemen t learning to handle the runtime uncertainties in self-adapt ive software,.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists Using reinforcemen t learning to handle the runtime uncertainties in self-adapt ive software,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:37:34.109216Z

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Observation d1794472-cf4c-4e14-81f6-789fe1b3c4be · outbound

This paper cites Safely enteri ng the deep: A review of veriﬁcation and validation for machine lea rning and a challenge elicitation in the automotive industry,.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists Safely enteri ng the deep: A review of veriﬁcation and validation for machine lea rning and a challenge elicitation in the automotive industry,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:37:34.098626Z

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 089b042b-aad0-4138-acf5-af53b84c9ef3 · outbound

This paper cites A machine learning ap proach to software requirements prioritization,.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists A machine learning ap proach to software requirements prioritization,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:37:34.087514Z

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 a8eed0d6-b73e-48b0-b68f-b51970f477e8 · outbound

This paper cites An act ive learning approach for improving the accuracy of automated domain mod el extraction,.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists An act ive learning approach for improving the accuracy of automated domain mod el extraction,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:37:34.075937Z

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 0b311800-222e-46fb-8384-b38d00ea53c2 · outbound

This paper cites Extractio n of system states from natural language requirements,.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists Extractio n of system states from natural language requirements,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:37:34.065880Z

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 6eb41871-e6af-4ea5-926f-2f73f1a63196 · outbound

This paper cites Automatic classiﬁcation o f requirements based on convolutional neural networks,.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists Automatic classiﬁcation o f requirements based on convolutional neural networks,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:37:34.055527Z

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 5a321800-270d-47c9-8e03-4dc70bcca09e · outbound

This paper cites On the automatic classiﬁcation of app reviews,.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists On the automatic classiﬁcation of app reviews,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:37:34.045154Z

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 257b4c44-dd4e-412f-a7dd-0e67a64ad942 · outbound

This paper cites Introduction to knowledge dis covery and data mining,.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists Introduction to knowledge dis covery and data mining,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:37:34.034817Z

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 a481eb16-196c-41b7-8d9f-56bd312366cc · outbound

This paper cites The CRISP-DM model: the new blueprint for d ata mining,.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists The CRISP-DM model: the new blueprint for d ata mining,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:37:34.024482Z

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 20f75077-2a52-47d3-9ff4-34fdadd1813b · outbound

This paper cites Non-functional requirements for machine learning: Chal- lenges and new directions,.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists Non-functional requirements for machine learning: Chal- lenges and new directions,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:37:34.014750Z

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 ed856c08-fc26-4747-ab7e-784123332ef0 · outbound

This paper cites A clas siﬁcation framework of uncertainty in architecture-based self-adap tive systems with multiple quality requirements,.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists A clas siﬁcation framework of uncertainty in architecture-based self-adap tive systems with multiple quality requirements,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:37:34.005091Z

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 3a57e12f-0531-45a4-9f33-63dc177bad5e · outbound

This paper cites Soft- ware engineering for self-adaptive systems. Lecture Notes in Computer Science, vol. 5525,.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists Soft- ware engineering for self-adaptive systems. Lecture Notes in Computer Science, vol. 5525,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:37:33.993763Z

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 268893eb-9f77-47be-bd84-f4b119435fda · outbound

This paper cites The vision of autonomic co mputing,.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists The vision of autonomic co mputing,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:37:33.981513Z

Source-reported events for the cited work

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Observation 7dba219e-f5dd-4b3c-ac83-ae70dbe2b4d4 · outbound

This paper cites Engineering requirements for adaptive systems,.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists Engineering requirements for adaptive systems,

Reference 17

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Source-reported events for the cited work

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Observation 7e8887a2-15df-4f95-be72-a048a8de54f2 · outbound

This paper cites Business-driven data analytic s: A conceptual modeling framework,.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists Business-driven data analytic s: A conceptual modeling framework,

Reference 18

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Observation 2de011b3-371f-4da4-b517-5b1f93527eaa · outbound

This paper cites Requirements enginee ring for health data analytics: Challenges and possible directions,.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists Requirements enginee ring for health data analytics: Challenges and possible directions,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:37:33.945596Z

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

Observation d452ae77-24de-4305-9038-7033715e2ad3 · outbound

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Requirements Engineering for Machine Learning: Perspectives from Data Scientists Unresolved cited work

Reference 20

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Observation c7a38f59-fda7-4d18-a376-32f5f65afbe5 · outbound

This paper cites an unresolved cited work.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists Unresolved cited work

Reference 21

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

Observation 68908754-041b-47a1-9f73-2f634f740a38 · outbound

This paper cites An investiga tion of how quality requirements are speciﬁed in industrial practice,.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists An investiga tion of how quality requirements are speciﬁed in industrial practice,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:37:33.915068Z

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Observation b4126536-9842-425f-bdc3-1fcf3d2d632e · outbound

This paper cites Ng, Machine Learning Yearning.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists Ng, Machine Learning Yearning

Reference 23

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Observation 58e518b7-d5e5-4d8e-917d-63ce95bc0893 · outbound

This paper cites Why should I tr ust you?: Explaining the predictions of any classiﬁer,.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists Why should I tr ust you?: Explaining the predictions of any classiﬁer,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:37:33.894372Z

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

Observation 7bb410d0-ed04-4d67-9f69-35eeca702823 · outbound

This paper cites What does my classiﬁer lea rn? A visual approach to understanding natural language text classiﬁer s,.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists What does my classiﬁer lea rn? A visual approach to understanding natural language text classiﬁer s,

Reference 25

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Observation f17df3db-3b16-4aa6-ac6a-defaf766d05d · outbound

This paper cites Systems and software engineering – Systems a nd software quality requirements and evaluation (SQuaRE) – System and s oftware quality models,.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists Systems and software engineering – Systems a nd software quality requirements and evaluation (SQuaRE) – System and s oftware quality models,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:37:33.872264Z

Source-reported events for the cited work

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Observation 11fe13ec-abba-4e70-8fc4-193c981b07e1 · outbound

This paper cites Women also snowboard: Overcoming bias in captioning model s,.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists Women also snowboard: Overcoming bias in captioning model s,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:37:33.862356Z

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 af93ed35-03f9-437e-9bd6-fcfb89818cd9 · outbound

This paper cites Barocas, M.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists Barocas, M

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:37:33.850738Z

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Observation bc3f7380-e8ea-46a7-8689-31f6e4f32f34 · outbound

This paper cites T he ML test score: A rubric for ML production readiness and technic al debt reduction,.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists T he ML test score: A rubric for ML production readiness and technic al debt reduction,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:37:33.840811Z

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

Observation 2d38850a-c61e-46bc-9d30-e9634ee4d6a5 · outbound

This paper cites Systems and software engineering – Systems a nd software quality requirements and evaluation (SQuaRE) – Data qualit y model,.

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:37:33.828952Z

Source-reported events for the cited work

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Observation 8784a9eb-6d59-48f4-bba5-3f1db8bd4585 · outbound

This paper cites Big data provenance: Challenges, state of the art and opportunities ,.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists Big data provenance: Challenges, state of the art and opportunities ,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:37:33.818001Z

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 33c0cb43-c3f2-4bcf-b988-3a425ea5c9e2 · outbound

This paper cites Evaluation of tools for hairy requirement s and software engineering tasks,.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists Evaluation of tools for hairy requirement s and software engineering tasks,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:37:33.807762Z

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 817df6d9-e7be-4ab2-9d0b-3ef9334f180a · outbound

This paper cites Optimizing for recall in automatic requirements classiﬁcation: An empirical study ,.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists Optimizing for recall in automatic requirements classiﬁcation: An empirical study ,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:37:33.795304Z

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 d88dedac-ac5f-4bd5-807d-7aa113c56914 · outbound

This paper cites Big data, fast data an d data lake concepts,.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists Big data, fast data an d data lake concepts,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:37:33.783461Z

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 6db06108-4b48-4048-83cd-61670c530370 · outbound

This paper cites Ten ways to fool the masses with machine learning.

Requirements Engineering for Machine Learning: Perspectives from Data Scientists Ten ways to fool the masses with machine learning

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-08-14T13:37:33.770679Z

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.

Pith citing papers

No inbound Pith citation observations are available.