PID,DOI,affectedQA,specificSubQAs
P12,https://arxiv.org/abs/1910.06136,Compatibility,Interoperability
P13,https://research.google/pubs/pub48984/,Compatibility,Interoperability
P03,https://dl.acm.org/doi/10.1145/3377815.3381377,Compatibility,All
P06,http://sites.computer.org/debull/A18dec/p5.pdf,Compatibility,All
P02,https://doi.org/10.1109/BigData.2017.8258038,Fairness,All
P10,https://doi.org/10.1145/3308560.3317590,Fairness,All
P16,https://doi.org/10.1007/978-3-030-19034-7_14,Fairness,All
P20,https://www.semanticscholar.org/paper/Data-Quality-Considerations-for-Big-Data-and-Going-Gudivada-Apon/625a9e9822603b79f754c4ce044760f7363b5eb6,Fairness,All
P07,https://doi.org/10.1109/SEAA.2018.00018,Functional suitability,"Functional appropriateness, functional correctness"
P16,https://doi.org/10.1007/978-3-030-19034-7_14,Functional suitability,"Functional appropriateness, functional correctness"
P19,https://doi.org/10.1109/REW.2019.00051,Functional suitability,"Functional appropriateness, functional correctness"
P01,http://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf,Functional suitability,"Functional completeness, functional correctness"
P03,https://dl.acm.org/doi/10.1145/3377815.3381377,Functional suitability,"Functional completeness, functional correctness"
P04,https://doi.org/10.1609/aimag.v41i1.5204,Functional suitability,"Functional completeness, functional correctness"
P02,https://doi.org/10.1109/BigData.2017.8258038,Functional suitability,Functional correctness
P05,https://doi.org/10.1007/978-3-030-35333-9_14,Functional suitability,Functional correctness
P06,http://sites.computer.org/debull/A18dec/p5.pdf,Functional suitability,Functional correctness
P08,https://doi.org/10.1145/3379597.3387479,Functional suitability,Functional correctness
P09,https://arxiv.org/abs/1606.03966,Functional suitability,Functional correctness
P12,https://arxiv.org/abs/1910.06136,Functional suitability,Functional correctness
P14,https://mlsys.org/Conferences/2019/doc/2019/167.pdf,Functional suitability,Functional correctness
P15,http://learningsys.org/nips17/assets/papers/paper_19.pdf,Functional suitability,Functional correctness
P17,https://doi.org/10.1109/SEAA.2019.00030,Functional suitability,Functional correctness
P18,https://doi.org/10.1145/3340482.3342743,Functional suitability,Functional correctness
P20,https://www.semanticscholar.org/paper/Data-Quality-Considerations-for-Big-Data-and-Going-Gudivada-Apon/625a9e9822603b79f754c4ce044760f7363b5eb6,Functional suitability,Functional correctness
P21,https://doi.org/10.1109/BigData47090.2019.9006187,Functional suitability,Functional correctness
P01,http://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf,Maintainability,"Analysability, modifiability, modularity, reusability, testability"
P02,https://doi.org/10.1109/BigData.2017.8258038,Maintainability,"Analysability, modifiability, modularity, reusability, testability"
P14,https://mlsys.org/Conferences/2019/doc/2019/167.pdf,Maintainability,"Analysability, modifiability, reusability, testability"
P07,https://doi.org/10.1109/SEAA.2018.00018,Maintainability,"Analysability, modifiability, testability"
P04,https://doi.org/10.1609/aimag.v41i1.5204,Maintainability,"Analysability, reusability"
P13,https://research.google/pubs/pub48984/,Maintainability,"Modifiability, modularity, testability"
P17,https://doi.org/10.1109/SEAA.2019.00030,Maintainability,"Modifiability, reusability"
P20,https://www.semanticscholar.org/paper/Data-Quality-Considerations-for-Big-Data-and-Going-Gudivada-Apon/625a9e9822603b79f754c4ce044760f7363b5eb6,Maintainability,"Modifiability, reusability"
P16,https://doi.org/10.1007/978-3-030-19034-7_14,Maintainability,"Modifiability, testability"
P19,https://doi.org/10.1109/REW.2019.00051,Maintainability,"Modifiability, testability"
P09,https://arxiv.org/abs/1606.03966,Maintainability,"Modularity, testability"
P03,https://dl.acm.org/doi/10.1145/3377815.3381377,Maintainability,All
P05,https://doi.org/10.1007/978-3-030-35333-9_14,Maintainability,All
P06,http://sites.computer.org/debull/A18dec/p5.pdf,Maintainability,All
P08,https://doi.org/10.1145/3379597.3387479,Maintainability,All
P12,https://arxiv.org/abs/1910.06136,Maintainability,All
P02,https://doi.org/10.1109/BigData.2017.8258038,Observability,All
P07,https://doi.org/10.1109/SEAA.2018.00018,Observability,All
P09,https://arxiv.org/abs/1606.03966,Observability,All
P10,https://doi.org/10.1145/3308560.3317590,Observability,All
P12,https://arxiv.org/abs/1910.06136,Observability,All
P16,https://doi.org/10.1007/978-3-030-19034-7_14,Observability,All
P17,https://doi.org/10.1109/SEAA.2019.00030,Observability,All
P19,https://doi.org/10.1109/REW.2019.00051,Observability,All
P07,https://doi.org/10.1109/SEAA.2018.00018,Performance efficiency,Resource utilization
P02,https://doi.org/10.1109/BigData.2017.8258038,Performance efficiency,"Resource utilization, time behaviour"
P03,https://dl.acm.org/doi/10.1145/3377815.3381377,Performance efficiency,All
P12,https://arxiv.org/abs/1910.06136,Performance efficiency,All
P18,https://doi.org/10.1145/3340482.3342743,Performance efficiency,All
P18,https://doi.org/10.1145/3340482.3342743,Reliability,"Availability, fault tolerance"
P04,https://doi.org/10.1609/aimag.v41i1.5204,Reliability,Maturity
P10,https://doi.org/10.1145/3308560.3317590,Reliability,Maturity
P14,https://mlsys.org/Conferences/2019/doc/2019/167.pdf,Reliability,Maturity
P17,https://doi.org/10.1109/SEAA.2019.00030,Reliability,Maturity
P20,https://www.semanticscholar.org/paper/Data-Quality-Considerations-for-Big-Data-and-Going-Gudivada-Apon/625a9e9822603b79f754c4ce044760f7363b5eb6,Reliability,Maturity
P02,https://doi.org/10.1109/BigData.2017.8258038,Reliability,All
P02,https://doi.org/10.1109/BigData.2017.8258038,Reproducibility,All
P09,https://arxiv.org/abs/1606.03966,Reproducibility,All
P10,https://doi.org/10.1145/3308560.3317590,Reproducibility,All
P16,https://doi.org/10.1007/978-3-030-19034-7_14,Reproducibility,All
P10,https://doi.org/10.1145/3308560.3317590,Responsibility,All
P11,https://doi.org/10.1007/978-3-030-49392-9_13,Responsibility,All
P04,https://doi.org/10.1609/aimag.v41i1.5204,Security,Accountability
P11,https://doi.org/10.1007/978-3-030-49392-9_13,Security,Accountability
P02,https://doi.org/10.1109/BigData.2017.8258038,Security,"Accountability, confidentiality"
P20,https://www.semanticscholar.org/paper/Data-Quality-Considerations-for-Big-Data-and-Going-Gudivada-Apon/625a9e9822603b79f754c4ce044760f7363b5eb6,Security,Confidentiality
P07,https://doi.org/10.1109/SEAA.2018.00018,Security,"Confidentiality, integrity"
P06,http://sites.computer.org/debull/A18dec/p5.pdf,Security,All
P18,https://doi.org/10.1145/3340482.3342743,Security,All
P07,https://doi.org/10.1109/SEAA.2018.00018,Transparency,All
P10,https://doi.org/10.1145/3308560.3317590,Transparency,All
P11,https://doi.org/10.1007/978-3-030-49392-9_13,Transparency,All
P16,https://doi.org/10.1007/978-3-030-19034-7_14,Transparency,All
P06,http://sites.computer.org/debull/A18dec/p5.pdf,Usability,Operability