Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors | Paper Citation Record

Observation 07b8dc3a-c69e-4653-ac02-23a5a1e9fc6d · outbound

This paper cites Data mining static code attributes to learn defect predictors,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Data mining static code attributes to learn defect predictors,

Reference 1

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This paper cites A brief note, with thanks, on the contributions of guenther ruhe,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors A brief note, with thanks, on the contributions of guenther ruhe,

Reference 2

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This paper cites The road ahead for mining software repositories,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors The road ahead for mining software repositories,

Reference 3

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This paper cites Foreword,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Foreword,

Reference 4

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This paper cites Replicating MSR: A study of the potential replicability of papers published in the mining software repositories proceed- ings,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Replicating MSR: A study of the potential replicability of papers published in the mining software repositories proceed- ings,

Reference 5

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This paper cites Revisiting the repro- ducibility of empirical software engineering studies based on data retrieved from development repositories,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Revisiting the repro- ducibility of empirical software engineering studies based on data retrieved from development repositories,

Reference 6

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Observation b92ee5b5-d3ea-45ca-8053-fa32cca43367 · outbound

This paper cites Tuning for software analytics: Is it really necessary?.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Tuning for software analytics: Is it really necessary?

Reference 7

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Observation a74890a7-09d7-4b66-8645-5c36ed34f327 · outbound

This paper cites Common trends in software fault and failure data,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Common trends in software fault and failure data,

Reference 8

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This paper cites An investigation into the functional form of the size-defect relationship for software modules,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors An investigation into the functional form of the size-defect relationship for software modules,

Reference 9

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Observation e2529eeb-b505-4c42-b536-a4bbe8dc43b5 · outbound

This paper cites Where the bugs are,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Where the bugs are,

Reference 10

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Observation 0300bf2a-0468-49f1-87a6-bd76d46227b0 · outbound

This paper cites Ai-based software defect predictors: Applications and benefits in a case study,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Ai-based software defect predictors: Applications and benefits in a case study,

Reference 11

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This paper cites Software measurement: a necessary scientific basis,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Software measurement: a necessary scientific basis,

Reference 12

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Observation 432dbf4e-403b-4a6b-b204-89312456e6d6 · outbound

This paper cites A complexity measure,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors A complexity measure,

Reference 13

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Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Unresolved cited work

Reference 14

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Observation 48fa6762-cd43-420c-af54-7ea0f1c1a111 · outbound

This paper cites an unresolved cited work.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Unresolved cited work

Reference 15

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Observation a57b517d-b7d6-4641-81f7-ff4e8f060e8e · outbound

This paper cites A critique of three metrics,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors A critique of three metrics,

Reference 16

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This paper cites When less is more: on the value of “co-training.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors When less is more: on the value of “co-training

Reference 17

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Observation 76e6566a-d346-4bd2-b450-cdb5eb3f03cc · outbound

This paper cites Comparing static bug finders and statistical prediction,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Comparing static bug finders and statistical prediction,

Reference 18

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Observation 21ff9aaf-7a93-44c2-87db-0180c0b3d4bf · outbound

This paper cites Percep- tions, expectations, & challenges in defect prediction,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Percep- tions, expectations, & challenges in defect prediction,

Reference 19

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Observation 97045920-eb5a-44e9-b499-98abac3edfb5 · outbound

This paper cites Remi: defect prediction for efficient api testing,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Remi: defect prediction for efficient api testing,

Reference 20

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Observation 1d4cd74f-fe80-4410-bfa9-cacf59ce6c44 · outbound

This paper cites Can traditional fault prediction models be used for vulnerability prediction?.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Can traditional fault prediction models be used for vulnerability prediction?

Reference 21

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Observation d711ca51-6a2e-4a40-bba7-904b439e4620 · outbound

This paper cites Putting it all together: Using socio-technical networks to predict failures,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Putting it all together: Using socio-technical networks to predict failures,

Reference 22

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Observation 2fcad18d-aa47-404f-a473-a76bb274cc57 · outbound

This paper cites Defect prediction from static code features: Current results, limi- tations, new approaches,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Defect prediction from static code features: Current results, limi- tations, new approaches,

Reference 23

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Observation 3a7f8f00-fd08-4f2d-8121-6ef8543cffc8 · outbound

This paper cites Data mining static code attributes to learn defect predictors,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Data mining static code attributes to learn defect predictors,

Reference 24

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Observation 97d29be6-f80b-49a3-bf1d-8c1146d3a596 · outbound

This paper cites An extensive compari- son of bug prediction approaches,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors An extensive compari- son of bug prediction approaches,

Reference 25

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Observation b986f625-bb5a-47b4-9ac7-e50e9b852d9b · outbound

This paper cites Use of relative code churn measures to predict system defect density,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Use of relative code churn measures to predict system defect density,

Reference 26

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Observation 38d44ba0-5ea3-4fd4-a14c-394bebc203ac · outbound

This paper cites Code churn: A measure for estimating the impact of code change,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Code churn: A measure for estimating the impact of code change,

Reference 27

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This paper cites A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction,

Reference 28

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This paper cites Predicting faults using the complexity of code changes,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Predicting faults using the complexity of code changes,

Reference 29

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Observation 9a62eaa0-5155-4ba7-8459-9e69c5999c23 · outbound

This paper cites Defect prediction: Accomplishments and future challenges,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Defect prediction: Accomplishments and future challenges,

Reference 30

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Observation 22ad3f65-7644-4f7f-856e-98580d2ca1f4 · outbound

This paper cites A systematic study of automated program repair: Fixing 55 out of 105 bugs for $8 each,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors A systematic study of automated program repair: Fixing 55 out of 105 bugs for $8 each,

Reference 31

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This paper cites A practical guide for using statistical tests to assess randomized algorithms in software engineering,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors A practical guide for using statistical tests to assess randomized algorithms in software engineering,

Reference 32

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This paper cites Applications of psychological science for actionable analytics,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Applications of psychological science for actionable analytics,

Reference 33

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Observation 04cdabb4-3c63-4f16-9d60-7418a08c6edd · outbound

This paper cites Is better data better than better data miners?: on the benefits of tuning smote for defect prediction,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Is better data better than better data miners?: on the benefits of tuning smote for defect prediction,

Reference 34

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Observation a1a02483-e9fa-4b71-8928-0ec23cefd237 · outbound

This paper cites Automating change-level self-admitted technical debt determination,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Automating change-level self-admitted technical debt determination,

Reference 35

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Observation 6679419e-ecf3-46a3-87e4-42575e22b176 · outbound

This paper cites A large-scale empirical study of just-in-time quality assurance,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors A large-scale empirical study of just-in-time quality assurance,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:06.056031Z

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This paper cites Clever: Combining code metrics with clone detection for just-in-time fault prevention and resolution in large industrial projects,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Clever: Combining code metrics with clone detection for just-in-time fault prevention and resolution in large industrial projects,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:06.044677Z

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

Observation 3630d157-2566-4a28-b37d-d714a557fe97 · outbound

This paper cites Commit guru: Analytics and risk prediction of software commits,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Commit guru: Analytics and risk prediction of software commits,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:06.033881Z

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 9467fef6-81b5-47cf-980f-47100d0480a7 · outbound

This paper cites Bellwethers: A baseline method for transfer learning,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Bellwethers: A baseline method for transfer learning,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:06.022984Z

Source-reported events for the cited work

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Observation 0ebb4a9e-7fbe-4f40-a8f1-fbd5aafa3250 · outbound

This paper cites Heterogeneous defect prediction,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Heterogeneous defect prediction,

Reference 40

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Observation 1feb01cf-c0fc-420a-bc59-bbc646891220 · outbound

This paper cites Revisiting the impact of classification techniques on the performance of defect prediction models,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Revisiting the impact of classification techniques on the performance of defect prediction models,

Reference 41

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Source-reported events for the cited work

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This paper cites What is wrong with topic modeling? and how to fix it using search-based software engi- neering,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors What is wrong with topic modeling? and how to fix it using search-based software engi- neering,

Reference 42

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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 239d0507-58e0-483c-af1d-e15c1d36ab6c · outbound

This paper cites Easy over hard: A case study on deep learning,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Easy over hard: A case study on deep learning,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.978392Z

Source-reported events for the cited work

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Observation f6ec6647-5833-4ae3-9b12-7e23c441b8d4 · outbound

This paper cites Why is differential evolution better than grid search for tuning defect predictors?.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Why is differential evolution better than grid search for tuning defect predictors?

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.968630Z

Source-reported events for the cited work

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This paper cites Software engineering economics,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Software engineering economics,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.947571Z

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 2bba0140-4803-46b2-8374-8b020b022bde · outbound

This paper cites Statistical analysis on the productivity of data processing with development projects using the function point technique,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Statistical analysis on the productivity of data processing with development projects using the function point technique,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.936746Z

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 7e8ba8e8-963c-45d9-91f6-9fd52c776354 · outbound

This paper cites How good is your blind spot sampling policy,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors How good is your blind spot sampling policy,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.924893Z

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 114e76a2-b796-4d40-96a4-716ee3eed42b · outbound

This paper cites Towards identifying software project clusters with regard to defect prediction,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Towards identifying software project clusters with regard to defect prediction,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.913417Z

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 f842e889-7480-452e-969d-ca28ad9a756c · outbound

This paper cites Sequential model op- timization for software effort estimation,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Sequential model op- timization for software effort estimation,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.901399Z

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 f65ea33c-f786-4870-bfca-040c2c11c63b · outbound

This paper cites Commit guru: analytics and risk prediction of software commits,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Commit guru: analytics and risk prediction of software commits,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.887407Z

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 ad04b0d8-a67a-4bb5-82c1-6d7f53ab9e1f · outbound

This paper cites Repro- ducibility and credibility in empirical software engineering: A case study based on a systematic literature review of the use of the szz algorithm,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Repro- ducibility and credibility in empirical software engineering: A case study based on a systematic literature review of the use of the szz algorithm,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.874868Z

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 9b2b15ef-ddd5-4c3d-96ba-e84259a452dd · outbound

This paper cites Problems with szz and features: An empirical study of the state of practice of defect prediction data collection,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Problems with szz and features: An empirical study of the state of practice of defect prediction data collection,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.863659Z

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 041bad94-9a0d-4d50-959d-7e295e136274 · outbound

This paper cites Deeplinedp: Towards a deep learning approach for line-level defect prediction,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Deeplinedp: Towards a deep learning approach for line-level defect prediction,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.853116Z

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 726cb54f-00bb-443e-9654-8d76e1305307 · outbound

This paper cites Explainable ai for software engineering,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Explainable ai for software engineering,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.842602Z

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 9ac2015d-d74d-4aae-80ca-cf38c20bca22 · outbound

This paper cites Fairway: a way to build fair ml software,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Fairway: a way to build fair ml software,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.832009Z

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 89164acc-9820-4edd-a0e5-cf71a6b6f8d5 · outbound

This paper cites Don’t lie to me: Avoiding malicious explanations with stealth,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Don’t lie to me: Avoiding malicious explanations with stealth,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.821641Z

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 a864ea0f-3fd8-4f9e-8678-15bf04656e4d · outbound

This paper cites Converging on the Optimal Attain- ment of Requirements,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Converging on the Optimal Attain- ment of Requirements,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.811434Z

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 ba152406-074d-4cf7-bb8c-e25786038454 · outbound

This paper cites How to avoid drastic software process change (using stochastic stability),.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors How to avoid drastic software process change (using stochastic stability),

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.800284Z

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 b12448f4-8c3f-4fb1-8a89-265409dd9d8d · outbound

This paper cites The business case for automated software engineer- ing,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors The business case for automated software engineer- ing,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.788076Z

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 d8619fed-71c2-40e7-a2a4-7421d6b6f250 · outbound

This paper cites Replication can improve prior results: A github study of pull request acceptance,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Replication can improve prior results: A github study of pull request acceptance,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.777040Z

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 475d8382-2305-4dcd-b926-647809c12461 · outbound

This paper cites Learning from very little data: On the value of landscape analysis for predicting software project health,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Learning from very little data: On the value of landscape analysis for predicting software project health,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.765806Z

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 a5954a42-c326-4021-ac8c-79181df68b09 · outbound

This paper cites Finding faster configurations using flash,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Finding faster configurations using flash,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.754084Z

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 3fe91304-526c-43fd-a548-ca96dc3aea43 · outbound

This paper cites Gale: Geometric active learning for search-based software engineering,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Gale: Geometric active learning for search-based software engineering,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.741078Z

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 2ebb1c0f-fc30-4d9a-8745-1cbaa5997cfd · outbound

This paper cites Finding better active learn- ers for faster literature reviews,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Finding better active learn- ers for faster literature reviews,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.729302Z

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 74144fa6-9938-4bdd-b717-8a33efc31e8c · outbound

This paper cites Frugal: unlocking semi-supervised learn- ing for software analytics,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Frugal: unlocking semi-supervised learn- ing for software analytics,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.716953Z

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 fbdc2a2e-0c20-4013-b4af-305b7dd4b0aa · outbound

This paper cites On the relative value of cross-company and within-company data for defect prediction,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors On the relative value of cross-company and within-company data for defect prediction,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.702235Z

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 f6e5ca74-fb28-4164-a416-68ab27cd4ac7 · outbound

This paper cites A survey on transfer learning,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors A survey on transfer learning,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.690814Z

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 f4373318-0c99-4350-9fc7-0aca1771dc2e · outbound

This paper cites Shockingly simple:.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Shockingly simple:

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.679754Z

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 e7873daf-ba50-452f-8c4d-34865f5a30c2 · outbound

This paper cites Better Predictors for Issue Lifetime.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Better Predictors for Issue Lifetime

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-10T14:07:05.271886Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28f8a4a7-846f-4039-8c00-351e11d83bd5 · outbound

This paper cites Ac- tive learning and effort estimation: Finding the essential content of software effort estimation data,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Ac- tive learning and effort estimation: Finding the essential content of software effort estimation data,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.668687Z

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 ed471a08-a639-491e-b205-d6155d7780c7 · outbound

This paper cites Lace2: Better privacy- preserving data sharing for cross project defect prediction,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Lace2: Better privacy- preserving data sharing for cross project defect prediction,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.657819Z

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 0181bfe5-bf9e-445b-acb6-3e1f98c70ef3 · outbound

This paper cites Finding the right data for software cost modeling,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Finding the right data for software cost modeling,

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-10T14:07:05.283944Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35eda5f4-58a0-4bde-aac7-80a9b707b5cf · outbound

This paper cites Semi-supervised learning literature survey,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Semi-supervised learning literature survey,

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-10T14:07:05.287365Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de95d9f3-cae7-4b5c-9c0d-04324d766031 · outbound

This paper cites Extensions of lipschitz map- pings into a hilbert space,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Extensions of lipschitz map- pings into a hilbert space,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.630654Z

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 f300e33c-34b7-466e-b11b-24f6e7e478a3 · outbound

This paper cites A comprehensive comparative study of clustering- based unsupervised defect prediction models,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors A comprehensive comparative study of clustering- based unsupervised defect prediction models,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.618237Z

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 480b4cab-0b0b-46db-aa1c-2b37a7afbea8 · outbound

This paper cites Implications of ceiling effects in defect predictors,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Implications of ceiling effects in defect predictors,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.606809Z

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 535bad7e-eb8b-4d3d-96b7-3628929ccf00 · outbound

This paper cites Why power laws? an explanation from fine-grained code changes,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Why power laws? an explanation from fine-grained code changes,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.591299Z

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 19875064-b416-4141-bc1e-a4a3b8661206 · outbound

This paper cites On the naturalness of software,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors On the naturalness of software,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.578827Z

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 896a325f-466f-432f-b65c-92a6a37c473a · outbound

This paper cites ”sampling.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors ”sampling

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.566022Z

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 ab61e563-3f18-44e3-83cf-78335710dada · outbound

This paper cites Large language models for software engineering: A systematic literature review,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Large language models for software engineering: A systematic literature review,

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-10T14:07:05.310741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2383e4b5-2445-41de-bc91-37d6a04b90f9 · outbound

This paper cites Why do tree-based models still outperform deep learning on typical tabular data?.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Why do tree-based models still outperform deep learning on typical tabular data?

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.551106Z

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 1a76576c-74dc-4df6-8929-bf643a3e2b26 · outbound

This paper cites A Survey on Deep Tabular Learning.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors A Survey on Deep Tabular Learning

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-10T14:07:05.317750Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 455ac9a1-c2fb-463e-83f2-b4909c93cc74 · outbound

This paper cites Agile effort estimation: Have we solved the problem yet? insights from a replication study,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Agile effort estimation: Have we solved the problem yet? insights from a replication study,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.539011Z

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 0ea6b02c-4735-4493-910d-5fbb9847f830 · outbound

This paper cites 500+ times faster than deep learning,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors 500+ times faster than deep learning,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.526936Z

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 7e92608a-a43a-4f84-8831-11cfb5eea8d8 · outbound

This paper cites Trading off scalability, privacy, and performance in data synthesis,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Trading off scalability, privacy, and performance in data synthesis,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.514569Z

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 6245addc-80de-4512-8333-979497b4250b · outbound

This paper cites Easy over hard: a case study on deep learning,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Easy over hard: a case study on deep learning,

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-10T14:07:05.331736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73d3e8c9-e881-4730-8a64-ca431603942b · outbound

This paper cites Ai over-hype: A dangerous threat (and how to fix it),.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Ai over-hype: A dangerous threat (and how to fix it),

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.501830Z

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 62db28d3-b56a-4641-9e42-e56dc0603e7e · outbound

This paper cites Data quality: Some comments on the nasa software defect datasets,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Data quality: Some comments on the nasa software defect datasets,

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.490167Z

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 9e778d58-d9d3-4a61-afc0-833012725793 · outbound

This paper cites Using bad learners to find good configurations,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Using bad learners to find good configurations,

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.478590Z

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 4e819d36-54b4-4f35-96ac-7f28ccd4a8df · outbound

This paper cites How to ”dodge.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors How to ”dodge

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.466946Z

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 c49b5a4d-721e-4edb-92a3-261c6b488d28 · outbound

This paper cites Automated parameter optimization of classification techniques for defect prediction models,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Automated parameter optimization of classification techniques for defect prediction models,

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:05.957975Z

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 b998f3c5-224f-49cc-ade8-d088d12d4ccd · outbound

This paper cites Tuning for software analytics,.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Tuning for software analytics,

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-10T14:07:05.352558Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc0fb20e-603d-49d8-94c5-dba1cbd180f8 · outbound

This paper cites an unresolved cited work.

Retrospective: Data Mining Static Code Attributes to Learn Defect Predictors Unresolved cited work

Reference 94

Resolution: unresolved; raw_fallback, observed 2026-08-10T14:07:05.454095Z

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.

Coverage vector

Reference resolution

Outbound references

Pith citing papers