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logseq/assets/INFSOF-D-23-00572_reviewer.edn
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:content {:text "Our study seeks to analyze the evolution of repository metrics in a population of open-source projects from Github."},
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:content {:text "temporal validity is not a concept broadly addressed by the scientific literature on Empirical Software Engineering"},
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:content {:text " it refers to the extent to which a representative sample collected during a particular time period accurately represents the population at a future point."},
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:content {:text "several examples of Empirical Software Engineering studies published in top-ranked journals and conferences still rely on rather old datasets"},
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:content {:text " Bavota et al. [46] studied the evolution of a Java subset of the Apache ecosystem for a period of 14 years, in terms of their size, dependencies among them, and other meta-data"},
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:content {:text " exponential increase in the size of the projects and the number of dependencies"},
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:content {:text " temporal validity in Software Engineering,"},
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:content {:text " our main contribution is a longitudinal study to evaluate a population of quality open-source projects over six years in order to provide insights on the matter"},
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:content {:text "e the evolution of temporal validity in a population of quality software projects"},
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:content {:text "Our aim is to estimate the probability that a dataset remains representative as time passes by"},
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:content {:text "2077 repositories"},
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:content {:text "statistical hypothesis testing and effect size measures to ensure that our results were valid from a statistical perspective"},
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:content {:text "α = 0.05"},
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:content {:text "Bonferronis correction"},
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:content {:text "The null hypothesis states that the observations median of all tested groups are equal. The test is applied to multiple groups simultaneously but cannot identify exactly where and how much the groups are statistically different. When the null hypothesis was rejected, i.e., the median among all the groups is statistically different, we ran a post-hoc pairwise test to identify the pairs of groups of observations that were different. To do this, we used the Dunns test[59], in which the null hypothesis states that there is no difference between group"},
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:content {:text "The “beginning of time” was the date of the snapshot retrieval. The event considered for the analysis was: “the snapshot is not representative”, referring to the time when a significant difference is detected between a snapshot and the snapshot of a following wave."},
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:content {:text " time is a factor that can potentially influence the distributions of repository data"},
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:content {:text "The general survival analysis showed it is likely that a population of Java projects will be representative for the first year (82.3%),"},
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