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A Drift Propensity Detection Technique to Improve the Performance for Cross-Version Software Defect Prediction

https://doi.org/10.1109/compsac48688.2020.0-154
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-540-28645-5_29openalex-snapshot
Learning with Drift Detection
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Concept drift detection based on Fisher’s Exact test
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TSTSS: A two-stage training subset selection framework for cross version defect prediction
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inferredcites →https://doi.org/10.1109/compsac.2019.00017openalex-snapshot
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inferredcites →https://doi.org/10.1109/icse.2015.91openalex-snapshot
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inferredcites →https://doi.org/10.1109/iske.2017.8258734openalex-snapshot
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inferredcites →https://doi.org/10.1109/issre.2014.35openalex-snapshot
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inferredcites →https://doi.org/10.1109/qrs.2016.33openalex-snapshot
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inferredcites →https://doi.org/10.1109/saner.2018.8330210openalex-snapshot
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inferredcites →https://doi.org/10.1109/tkde.2018.2876857openalex-snapshot
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inferredcites →https://doi.org/10.1109/tr.2018.2847353openalex-snapshot
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inferredcites →https://doi.org/10.1109/tse.2016.2543218openalex-snapshot
HYDRA: Massively Compositional Model for Cross-Project Defect Prediction
inferredcites →https://doi.org/10.1109/tse.2017.2770124openalex-snapshot
A Systematic Literature Review and Meta-Analysis on Cross Project Defect Prediction
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Towards identifying software project clusters with regard to defect prediction
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inferredcites →https://doi.org/10.1145/2884781.2884839openalex-snapshot
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inferredcites →https://doi.org/10.1145/3092566openalex-snapshot
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inferredcites →https://doi.org/10.1145/3196321.3196331openalex-snapshot
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inferredcites →https://doi.org/10.1145/3273934.3273938openalex-snapshot
Cross-Version Defect Prediction using Cross-Project Defect Prediction Approaches
inferredcites →https://doi.org/10.2307/1165329openalex-snapshot
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inferredcites →https://doi.org/10.2307/1270020openalex-snapshot
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inferredcites →https://doi.org/10.3102/10769986025002101openalex-snapshot
A Critique and Improvement of the CL Common Language Effect Size Statistics of McGraw and Wong

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4536609openalex-extracted
Detecting Concept Drift for the Reliability Prediction of Software Defects Using Instance Interpretation
inferred← citeshttps://doi.org/10.1002/smr.2625openalex-snapshot
Evolutionary measures and their correlations with the performance of cross‐version defect prediction for object‐oriented projects
inferred← citeshttps://doi.org/10.1007/s10664-024-10502-3openalex-snapshot
Industrial adoption of machine learning techniques for early identification of invalid bug reports
inferred← citeshttps://doi.org/10.1016/j.array.2026.101239openalex-snapshot
A novel cross version software defect prediction method based on adversarial validation strategy
inferred← citeshttps://doi.org/10.1016/j.asoc.2021.107870openalex-snapshot
Inter-release defect prediction with feature selection using temporal chunk-based learning: An empirical study
inferred← citeshttps://doi.org/10.1145/3545258.3545275openalex-snapshot
Evolutionary Measures for Object-oriented Projects and Impact on the Performance of Cross-version Defect Prediction
inferred← citeshttps://doi.org/10.14569/ijacsa.2022.0130541openalex-snapshot
Effective Prediction of Software Defects using Random-tree Entropy based Feature Selection Framework
inferred← citeshttps://doi.org/10.3390/sym14122508openalex-snapshot
CODE: A Moving-Window-Based Framework for Detecting Concept Drift in Software Defect Prediction
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