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Dataset . 2021
License: CC BY
Data sources: Datacite
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Dataset . 2021
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Data sources: ZENODO
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Characterizing Technical Debt and Antipatterns in AI-Based Systems: A Systematic Mapping Study

Authors: Bogner, Justus; Verdecchia, Roberto; Gerostathopoulos, Ilias;

Characterizing Technical Debt and Antipatterns in AI-Based Systems: A Systematic Mapping Study

Abstract

All artifacts related to a systematic mapping study on technical debt and antipatterns in AI-based systems; the data consists of an Excel file (00-all-data.xlsx) which includes a tab for all important constructs plus a separate CSV file per construct: List of primary studies (01-primary-studies.csv) Established types of technical debt (02-established-td-types.csv) New types of technical debt (03-new-td-types.csv) Affected software quality attributes (04-affected-qas.csv) Identified antipatterns (05-antipatterns.csv) Reported solutions to address technical debt or antipatterns (06-solutions.csv)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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