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This replication package contains the dataset and script used in our paper "Learning Features that Predict Developer Responses for iOS App Store Reviews." The paper has been accepted at the ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), 2020. For further modification and versioning of the dataset (as well as the preprint) please go to https://github.com/Kamonphop/ESEM20-Replication
feature importance, user review, developer response, random forest
feature importance, user review, developer response, random forest
| 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). | 0 | |
| 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. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 5 |

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